About Leif Bjørnø International Students Award Leif Bjørnø International Student Award “FOR BEST GRADUATE STUDENT PAPERS“ was established in 2017 as a part of Underwater Acoustics Conference, - UACE - to boost the interest in Underwater Acoustics from young scientists. Since 2017 a competition among graduate students on the presentation of the best papers is arranged during the Conference, and Leif Bjørnø International Student Award presented at the closing ceremony of the Conference. First, second and third place are honored with medals and diplomas, and best paper is honored with students grant. History of Leif Bjørnø International Student Award appearance UACE 2017 was dedicated to the memory of Leif Bjørnø, cofounder and co-chairman of Underwater Acoustic Conference Series since 2005. He passed away unexpectedly on 24 of October 2015. First time Leif Bjørnø International Student Award was presented during the Underwater Acoustic Conference in 2017 in Skiatos, where his cremated body of Leif Bjørnø was spread at sea as a symbol og unification of the great scientist with World Ocean (partly, the same ceremony took place in Danmark, close to island Bornholm, performed by military ship, in autumn 2017). The Scientific Committee and the Organizers of UACE2017 were able to continue the long history of successes of the ECUA, UAM and UACE Conferences after leif Bjørnø death. This international event has always attracted a large number of scientists and engineers from Europe, the United States, Canada, China, Japan etc, and have become an established forum for the presentation of the latest developments in all major areas of Underwater Acoustics.
About professor Leif Bjørnø
Leif Bjørnø, was a leading researcher in ultrasonics and underwater acoustics. Leif Bjørnø held the position of Professor at the Department of Industrial Acoustics (Technical University of Denmark, Lyngby, Denmark) for over 22 years. He left the University in 2000 due to his commitments as a serving member or chairman of the board of directors or trustees of several institutions in Denmark and abroad. Leif’s activities were always internationally focused and centered on translational applications of science and technology. His experience included industrial applications of acoustics, especially in the field of machinery noise, mathematical models describing propagation of acoustic waves in different media, signal processing, development of ultrasound measurement devices, and underwater- and nonlinear- acoustics.
! Picture: Leif Bjørnø during his last conference UACE on Corfu in 2015, a few month before his death. He authored or co-authored over 400 scientific papers in peer-reviewed journals, professional magazines, conference proceedings and text-books. Professor Leif Bjørnø has completed his compulsory military service as a lieutenant in the Danish Navy and nurtured his Navy connection throughout his life - he was regularly called upon as consultant in the matters related to – particularly – his expertise in littoral acoustics. His expertise in acoustics was also highly recognized by the NATO, and during the cold war period he was considered to be one of the best internationally recognized experts in the field of underwater explosions, underwater sound propagation and underwater vibrations. As a result he was appointed as the leader of the Advisory Committee at NATO research center (SACLANT, La Spezia, Italy) – the Center’s priorities at that time were focused on anti-submarine warfare and mine detection. Leif Bjørnø was highly recognized as a scientist and human being both in Denmark and globally. He was appointed to a number of honorary (chairman) positions; he was also a member of many professional organizations and recipient of a number of awards and prizes. He never retired and at the time of his death held appointments as Visiting Professor at several European and American universities. In addition, he continued with his involvement, participation and planning of international scientific meetings. His peers considered him as a scholarly talented, life enjoying, open minded, honest and loyal individual, who stayed in touch and cultivated close connections with people he liked and respected. He disliked those, who were scholarly competent but incapable of treating others with due respect. These characteristics of Leif Bjørnø played undoubtedly a major role in his development of large, international network of friends, which included college peers, (Navy) officers friends and industry leaders and decision makers ("movers and shakers” as he jokes), and
younger friends that also enjoyed his company, hospitality, knowledge and good sense of humor. Reference: https://en.wikipedia.org/wiki/Leif_Bj%C3%B8rn%C3%B8
Leif Bjørnø International Students Award 2017 First 3 medals and diplomas were held to the students during the closing ceremony of the UA Conference in 2017, Skiatos, Greece. The Scientific Committee for the international Students Award were the following: Professor David Bradley (chairman), professor Michael Taroudakis, Research Professor, Director, Center for Acoustics Research and EducationJennifer L. Miksis-Olds.
The three Student award winners were: 1)
Tannaz Haji Mohammadloo (Delft University of Technology) - “Assessing the repeatability of sediment classification method and the limitations of using depth residuals”,
2)
Jodi Walsh (University of Exeter & University of Bath) - “Acoustic life cycle assessment of offshore renewables – Implications from a wave-energy converter deployment in Falmouth Bay, UK”
3)
Dezhi Wang (Academy of Marine Science and Engineering, National University of Defense Technology, Changsha, China) - “Classification of Whale Calls based on Transfer Learning and Convolutional Neural Network”.
! In the picture: PhD student Jodi Walsh beat off stiff competition to win 2nd place at the Underwater Acoustics Conference. Irina Bjørnø, assoc. prof. hold the speech for the winner during the ceremony (left in the picture) Her presentation called 'Acoustic Lifecycle Assessment of Offshore Renewables Implications from a Wave-energy Convertor Deployment in Falmouth Bay, UK'. Named in honour of Professor Leif Bjørnø, a pioneer in underwater acoustics and co-organiser of the
UAC until his death in 2015, the prize recognises students who have performed excellently in science, and conveyed their findings well at an international level. Jodi said in her interview: "Participating enabled me to meet scientists from all over the world, and appreciate the wide diversity of the applications of underwater acoustics as well as the complexities of the underlying physics. I'm very proud to have received one of the first Leif Bjørnø International Student Awards, especially after having read so many of his articles when starting out.” "The judging committee was very impressed with Jodi's performance, and she has since been invited to publish the next stages of this work by one of the leading publications in this arena.” - said Jodi’s superviser. Reference: https://www.bath.ac.uk/announcements/bath-physicist-jodi-walsh-wins-prestigious-leif-bjrnaward/
Papers by winners Leif Bjørnø International Student Awards 2017
UACE2017 - 4th Underwater Acoustics Conference and Exhibition
CLASSIFICATION OF WHALE CALLS BASED ON TRANSFER LEARNING AND CONVOLUTIONAL NEURAL NETWORK
Hao Yuea, Dezhi Wanga*, Lilun Zhanga, Yanqun Wua, and Changchun Baoa a
Academy of Marine Science and Engineering, National University of Defense Technology, Changsha, China *
Dezhi Wang, National University of Defense Technology, Changsha, China, 410073 wang_dezhi@hotmail.com
Abstract: As a primary communication method, whale vocal calls contain valuable information and abundant characteristics that are important for recognition and classification. However, subject to large variations of call types, there is still a great challenge to accurately categorize the different whale species or subpopulations. In this study, an effective method of transfer learning based on a data-driven machine-learning approach i.e. Convolutional Neural Network (CNN) is developed to extract the significant features of whale calls and classify the different whale categories from a large open-source acoustic dataset recorded by audio sensors carried by whales .The results show that the proposed method can achieve 97.04% and 91.47% in accuracy respectively to categorize the calls into the two whale species and the four whale subpopulations. The phylogeny graph is also produced to illustrate the similarities between the whale subpopulations. Moreover, all the results are carefully compared with those obtained by using the Wndchrm scheme and the Fisher discriminant scores on the same dataset. Keywords: Convolutional Neural Network (CNN)ďźŒtransfer learning, classification of whale calls, similarity analysis
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1. INTRODUCTION As the common species of whales in the ocean, Killer whales (Orcinus orca) and pilot whales (Globicephala) have been continually studied for over three decades. The two types of whales are both gregarious living within socially stable family units known as ‘pods’. Within a pod, whales share a unique repertoire (also known as dialect) of stereotyped calls, which are comprised of a complex pattern of pulsed and tonal elements [1]. With the continuous development of devices such as hydrophones deployed from ships, or digital acoustic recording tags (DTAGs) placed on marine mammals, large datasets of whale sound sample are acquired increasingly. In 2014, Lior Shamir etc. [2] proposed an automatic method for analyzing large acoustic datasets from the Whale FM project [3] and studied the differences between sounds of different subpopulations of whales. In their study, the significant features of whale calls were extracted by using Wndchrm [4] for biological image analysis and Fisher Discriminant Scores algorithm. These features are used to classify or evaluate the similarity between the different populations of samples without expert-based auditing. Though this work has already made a progress in the unsupervised classification and similarity analysis of large acoustic datasets of whale calls, it still highly relies on the effectiveness of different polynomial decomposition techniques and the Fisher scores algorithm. Nowadays, as a class of highly non-linear machine learning models, Convolutional Neural Networks (CNNs) become very popular after achieving state-of-the-art results for image recognition [5], for example, the Google Inception-v3 model [6]. The purpose of this study is to apply pre-trained Inception-v3 for transfer learning and efficiently extracting the informative features from large datasets of whale calls for classification and clustering. The approach is carefully described and the whale phylogeny is also produced. All the results are also compared with those presented in the work [2].
2. MATERIALS The datasets are obtained from the Whale FM website [3] which is a citizen science project from Zooniverse and Scientific American. All the datasets were collected by the recording DTAG [7], which can be attached to individual whales to record the sounds the whale makes as well as calls from other animals nearby. It also has motion sensors that allow following the movement of the whale underwater. The dataset consists of about 10,000 MP3 audio files ranging between 1s to 8s in 16 separate recording events based on the sensors carried by 7 pilot whales and 9 killer whales close to the coasts of Norway, Iceland, and the Bahamas. The other description about the data is shown in [2].
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3. APPROACH Transfer learning is a machine learning method which utilizes a pre-trained neural network without training models from beginning. In this study, we use Inception-v3 model trained by Google to learn the spectrums converted from the audios of whale calls and achieve the purpose of classification or identification. It contains two main parts that are feature extraction part using CNN networks and classification part using fully-connected networks. A more detailed overview of Inception-v3 can be referred to [6]. The Inception-v3 used in our work could be downloaded from Google [8]. In addition, our implementation is carried out based on Tensorflow (Version 1.1.0 on CPU), which is an open-source software library for Machine Learning using data flow graphs. Besides classification, the similarity analysis is also carried out in terms of the phylogeny produced by the distances matrix among the abstract features of different pods. The original audio files are firstly converted to 2D spectrograms shown as Fig. 1 for the subsequent analysis. In detail, the 2D spectrograms of the audio files were created using the STFT function in MATLAB (Hanning Window = 256; Overlap = 128; FFT size = 1024; FS = 20000). And all the images are RGB style with the resolution of 256*256*3. (‘3’ refer to RGB channels). The spectrograms are varied even if the calls are emitted by the same whale since a single whale could send a variety of sounds, which also increases the difficulties of classification. As a kind of deep learning method, CNN is more appropriate to process large datasets. It is necessary to prepare sufficient data to fully stimulate the CNN network, for which we increased the data amount artificially just by adjusting the image contrast with the function ‘imadjust’ in MATLAB.
Fig. 1: Spectrogram of a sample of whale calls
Fig. 2: The phylogeny created based on the CNN feature vectors
4. IMPLEMENTATION
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4.1 Experiment 1: Classification into the pilot and killer whales In order to classify the whale calls into the two species as the pilot and killer whales, all the MP3 audios are divided into two groups. One group consists of 5112 samples while another one consists of 4188 samples. Randomly, we choose 10% samples for validation, and 30% for testing, the rest samples are all used to retrain model. During the training process, the learning rate is set to 0.01 and the batch is set to 100. Finally, the training process will finish when the step achieves 10000. 4.2 Experiment 2: Classification into four subpopulations This experiment aimed at separating dataset into four subpopulations by different species and habitats, they are, Norwegian killer whales, Iceland killer whales, Bahamas short-finned whales and Norwegian long-finned whales. Since the number (about 1000) of MP3 files of some pods is significantly smaller than that of the others (about 3000), we expand their amounts artificially by adjusting the image contrast of the 2D spectrograms in Matlab. After the adjustment, the number of spectrograms of each subpopulation reaches 3000. Thus totally we have 12000 samples for the four subpopulations. Similarly, 10% samples are selected randomly for validation, 60% samples for training and the rest 30% for testing. The CNN structure is as the same as one used in the last section. 4.3 Experiment 3: Classification into 16 different pods Similarly, the image contrast of the spectrograms of each pod is adjusted so that the amounts of samples in each pod could reach 800. Thus the total number of samples of the 16 pods is 12800. Then 10% samples are selected for validation, 60% for training and the rest 25% for testing. 4.4 Experiment 4: Similarity analysis and the phylogeny The classifier of Inception-v3 used in this study is a normal full-connected neural network, which is a classifier based on the Softmax function. When we have 16 classes (pods), a probability vector with the size of 1Ă—16 would be the output for each sample. Each element (within a range [0, 1]) in the probability vector can be considered as a measure for the likelihood of the sample classified into the corresponding class or, in other words, the similarity between the sample and the class. Thus the probability vectors are reasonable to be used to evaluate the similarities of the whale calls in different pods. Totally 4, 300 samples are selected randomly from each pod to achieve the corresponding probability vectors. The row elements of all the probability vectors of each pod are summed up to achieve a 1Ă—16 general probability vector, which is employed to indicate the similarities between one pod and all the other pods. Then, we regard the probability vectors as the abstract feature of corresponding classes, and calculate their Square Euclidean distance each other to describe their similarity each other.
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In Lior Shamir’s study [2], in order to visualize the relations between the different subpopulations of whales, the phylogeny inferred by the Phylip software [9] is obtained based on the Minkowski distance between the selected polynomial decomposition descriptors of different pods, as shown in [2]. In our experiment, the phylogeny is also obtained by using Phylip but on the basis of the Square Euclidean distance of the extracted general probability vectors. In detail, we mainly use Fitch-Margoliash method and n-Body algorithm to create and improve the outtree respectively, the other parameters are defaults.
5. RESULTS 5.1 Classification As shown in Table 1, the classification accuracies corresponding to the two, four and 16 classes are listed and compared. It is worth noting that the reason why the 73.5% accuracy reached when classifying samples into 16 classes is that the samples from different pods might be the same whale subpopulations with very similar features. It results in a great difficulty to perfectly classify the samples. In this case, the similarity analysis or clustering analysis might be a more appropriate way to study the differences between the pods. About computational efficiency, we just use single Intel® Xeon® E7-4830 V2@2.20HZ CPU to retrain our dataset in our work. Averagely, it takes 20～40 min to deal with the dataset, and about 0.1s per step for training. So, it just needs about 15min when finishing the 10000 iterations. Methods
Wndchrm
CNN
Input data
2D Spectrograms
2D Spectrograms
Feature extraction
Polynomial decomposition methods and Fisher Scores algorithm
Inception-v3
92%
97.04%
×
91.47%
44%～62%
73.50%
Phylogeny shown in [2]
Phylogeny (Fig. 4)
Classification to two species Classification to four subpopulations Classification to 16 pods Similarity
Table 1: Comparison between Lior Shamir’s study and our work 5.2 Similarity analysis As shown in Fig. 2, the phylogeny graph can be clearly divided into pilot whales and killer whales along the middle dashed line. Also the influence of the geographic locations on the whale subpopulations is completely distinguished by our method as there are four distinct branches displayed. Specifically, there are two obvious points
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which clearly indicate the bifurcation of different subpopulations. For example, the bifurcation point ‘a’ is highlighted for the killer whales in different regions, from where the left branches are Norwegian killer whales and the right are Icelandic killer whales. Compared with the phylogeny in [2], our result is apparently more distinct and informative. The detailed comparison is shown in Table 1. 6. CONCLUSION The main purpose of this study is to use the Convolution Neural Network (CNN) to analyze large acoustic datasets and study differences between sounds of different whale subpopulations. The results show that the transfer model Inception-v3 is able to accurately classify these datasets into different categories in a supervised fashion. As shown in Table 1, the accuracy is obviously better than that in [2]. In general, in the case of sufficient dataset, the effect of transfer learning is not as good as complete re-training, but the data and time required for transfer learning are far less than complete re-training. Therefore, it is favorable to do the underwater target recognition with insufficient dataset. Moreover, transfer learning can present great efficiency although there are no high performance GPU devices for general users. The other purpose of this study is to characterize the similarities between the whale calls of different subpopulations in an unsupervised fashion. When applying the CNN to the datasets, we do not need to tag these datasets to certain classes but only randomly label each dataset with an ID. In this way, the informative features of the datasets can be extracted by the CNN algorithm for the similarity analysis. In [2], only 15% to 20% polynomial decomposition descriptors are selected by Fisher scores criterion to do the similarity analysis. In contrast, the features obtained by CNN more concisely represent the original data. The phylogeny graph is also produced based on the abstract CNN features in order to achieve a better understanding of the relations between whale subpopulations, which is also more distinct than the one shown in [2]. Thus CNN algorithm is shown to be an effective solution for the classification and similarity analysis of large acoustic datasets. 7. ACKNOWLEDGEMENTS This study was funded by the National Key Research and Development Project of China (No. 2016YFC1401800) and the Scientific Research Project of NUDT (No.ZK16-03-46, No.ZK16-03-31).
REFERENCES [1]. Miller, P.J.O. and D.E. Bain, Within-pod variation in the sound production of a pod of killer whales, Orcinus orca. Animal Behaviour, 2000. 60(5): p. 617-628. [2]. Shamir, L., et al, Classification of large acoustic datasets using machine learning and crowdsourcing: application to whale calls. Journal of the Acoustical Society
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of America, 2014. 135(2): p. 953-62. [3]. See the project at https://whale.fm and obtain the supplementary whale dataset used in the study at: https://github.com/zooniverse/WhaleFM/blob/master/csv/whale_fm_anon_04-032015_assets.csv [4]. Shamir, L., et al, Knee x-ray image analysis method for automated detection of osteoarthritis. Biomedical Engineering IEEE Transactions on, 2009. 56(2): p. 407. [5]. Krizhevsky, A., I. Sutskever, and G.E. Hinton, ImageNet classification with deep convolutional neural networks. in International Conference on Neural Information Processing Systems. 2012. [6]. Szegedy C, Vanhoucke V, Ioffe S, et al, Rethinking the Inception Architecture for Computer Vision[J]. Computer Science, 2015:2818-2826. [7]. Johnson, M., et al, A digital acoustic recording tag for measuring the response of marine mammals to sound. Journal of the Acoustical Society of America, 2003. 108(5): p. 2582-2583. [8]. Download the Inception-v3 at: https://storage.googleapis.com/download.tensorflow.org/models/inception_dec_2 015.zip [9]. Felsenstein, J, PHYLIP: phylogeny inference package. The Quarterly Review of Biology, 1989. 5(Volume 64, Number 4): p. 164-166.
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Leif Bjørnø International Student awards 2019
In 2019 Judging Committee for Leif Bjørnø International Student Award in 2019 was the following: Jennifer L. Miksis-Olds, Research Professor, Director, Center for Acoustics Research and Education (chairperson), professor David Bradley, professor Michael Taroudakis. And the Student prices were the following: Best paper and Student Grant (First Place in competition) - Gold Medal: 1. David Geroski for the paper: “Out of Band Signal Processing for Localizing Distant Sources in a Noisy Deep Ocean” Second Place: (Silver medal and diploma) 2. Filippo Campagnaro for the paper: “Data collection in shallow fresh water scenarios with low cost underwater acoustic modems”
3. 3th place and bronze medal: Jonas von Pein for the paper: “Identification of high noise zones with a 3D pile driving noise model”


It was 3 nominee who were honored with “Leif Bjørnø Badges” 1. Ernst Uzhansky “Variability of the sound field in the presence of internal Kelvin waves in a stratified lake” 2. Tannaz Haji Mohammadloo “Assessment of Reliability of Multi-Beam EchoSounder Bathymetric Uncertainty Prediction Models” 3. 6 Muriel Dunn “Development of acoustic Doppler fish monitoring for applications in high-energy tidal channels”
Papers published by winners of Leif Bjørnø Student Awards 2019
UACE2019 - Conference Proceedings
BELOW-BAND SIGNAL PROCESSING FOR LOCALIZING DISTANT SOURCES IN A NOISY DEEP OCEAN
David J. Geroskia, David R. Dowlingb a
David J. Geroski (ATTN: Alesha Thomas) Applied Physics Program 1301 Beal Avenue Ann Arbor MI, 48109 b David R. Dowling Department of Mechanical Engineering 1231 Beal Avenue Ann Arbor, MI 48109 Abstract: Frequency Difference Source Localization (FDSL) methods have proven successful for localizing sources both in the deep ocean and in the shallow ocean using recordings from a vertical array of hydrophones. These source localization algorithms are based upon the phase coherence of a product of recorded complex field values, termed autoproducts, at frequencies below the recordings’ bandwidth. Similar to Matched Field Processing (MFP), the measured autoproduct is correlated to a replica that is calculated based on the user's knowledge of the acoustic environment. These below-band source localization methods are nonlinear and sacrifice in-band spatial resolution for robustness to environmental mismatch. Past frequency-difference localization efforts utilized high signal-to-noise ratio (SNR) data to demonstrate robustness to mismatch. This paper explores the performance of FDSL in the presence of noise. Specifically, simulated noise and noise measured during the PhilSea10 experiment are added to both simulated and measured pings to determine the SNR below which localization performance begins to degrade. For the scenario considered here, the source was located 210 km from a vertical receiving array in a deep ocean environment with an active internal wave field. The source broadcast was a linear frequency sweep from 200 to 300 Hz. At high SNR (>20 dB) using single-digit-Hertz below-band frequencies, the source is correctly localized in all simulations, and in 97 out of 100 trials using single-broadcast ocean recordings. For simulated and measured forward signals distorted by simulated noise, this performance persists down to SNR's of –20 and –14 dB, respectively. For measured forward signals with elevated levels of measured noise, successful performance persists down to –8 dB SNR. Keywords: Source Localization, Nonlinear Signal Processing, Matched Field Processing
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1. INTRODUCTION Passive acoustic localization of a distant unknown source is a common task in acoustic remote sensing in the ocean using hydrophone arrays (see Dowling and Sabra, 2015). One popular class of frequency-domain techniques for this task is Matched Field Processing, (MFP) which involves correlating measured fields with replica fields calculated using the known propagation physics of the acoustic environment (Bucker, 1976). MFP techniques have proven to be robust to many kinds of ocean noise (Debever and Kuperman 2007), and they work well when the acoustic environment between the source and receiving array is well known. However, a lack of sufficiently-complete environmental knowledge causes MFP to struggle in many scenarios of interest (Baggeroer et. al. 1993). This problem, known as mismatch, becomes more severe at higher frequencies and longer ranges, as the acoustic propagation becomes more sensitive to smaller environmental details at higher frequency, and includes larger regions of the imperfectly-known ocean environment at longer range. Recently, this limitation has been mitigated in the shallow ocean using Frequency Difference MFP (FDMFP) to localize sources operating at high (f > 10 kHz) frequencies in a 103 m deep sound speed channel (Worthmann 2015, 2017). This technique involves constructing quadratic products of the measured signals and correlating them to lower frequency fields, where mismatch is less problematic. This article has two purposes: to introduce a mild revision of FDMFP, termed Frequency Difference Source Localization (FDSL), that allows distant acoustic sources to be localized in the deep ocean, and to test the noise rejection capabilities of FDSL. This performance is assessed with propagation data measured during the PhilSea10 experiment (Worcester et. al. 2013). In particular, this study focuses on localizing a single source, positioned 210 km downrange from a vertical array of transducers, that transmitted a frequency sweep signal (200 < f < 300 Hz) repeatedly over the course of several months. The results show that FDSL can be used to overcome mismatch in the deep ocean even at relatively low signal-to-noise ratios (SNRs). The remainder of this paper is divided into three sections. Section 2 contains a summary of conventional MFP and FDSL, as well as descriptions of the signal processing techniques undertaken to produce the given results. Section 3 provides a brief description of the PhilSea10 experiment, the post processing used to artificially degrade the SNR achieved during the experiment, and the simulated and measured results of FDSL in this ocean environment. Section 4 summarizes this study, and states the conclusions drawn from it. 2. THEORY The FDSL technique can be thought of as a variant of conventional MFP for array recorded acoustic signals. Given this, the most natural place to begin a discussion of this algorithm is with conventional, or Bartlett MFP (see Jensen et. al. 2011). Bartlett MFP is a spatial filtering process applied over a receiving array. Acoustic signals measured along the array from a source located at xs, denoted in the frequency domain as Pj(ω) at angular frequency ω and for hydrophone j, are cross correlated with a weight vector wj(xt,ω) that represents the acoustic response of the (known) acoustic environment to a time-harmonic source transmitting from trial location xt to hydrophone j. The trial-source location, xt, is then varied over the region of interest to produce an ambiguity surface defined in (1). (1)
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(2) (3) Here, Rij(ω) in (2) is conventionally referred to as the cross-spectral density matrix (CSDM) (Baggeroer et. al. 1993) and is defined as such to make MFP independent of the source waveform. The asterisk in (1) and (2) indicates complex conjugation, and G is the calculated Green’s function of the (known) environment for a source transmitting at xt to a receiver at xj. When defined this way, the ambiguity surface, Bc, is a positive, real, and bounded between 0 and 1, with 1 indicating a perfect match between the measured and calculated fields. Mismatch arises in (1) when Pj(ω) and wj(xs,ω) differ unintentionally. These differences tend to increase with frequency and source-receiver range. Some of these differences can be compensated by incoherently averaging through the measured signal bandwidth as in (4). Here the signal frequency is assumed to be bounded between ΩL and ΩH. (4) Frequency difference techniques, including FDSL, rely on analysing the CSDM of the frequency-difference autoproduct defined in (5) that is measured along the array, and correlating it to a weight vector that matches the physics of the autoproduct.
(5) (6) (7) (8) This processing is done by first taking a single sample of the autoproduct, as in (6), where the complex field values measured on one receiver are multiplied to form an autoproduct sample. This sample is then used to compute an autoproduct CSDM, and all samples available in the measured signal bandwidth are averaged and normalized to produce the final CSDM in (5). This CSDM is then cross correlated with the weight vector defined in (7). In (7), the amplitude of the weight vector is calculated using KRAKEN, a normal mode code, (Porter 1987), while its phase is derived from a ray code, Bellhop (Porter 1992). The amplitude follows that of FDMFP (Worthmann 2015), but the phase is derived from considerations of (6) when the measured field is well described as a sum of ray-path contribution with amplitude Ai and arrival times τi. The utility of the phase correction employed here is to remove the phase induced on a ray path which has been reflected by a barrier (Kinsler et. al. 2000) or which has passed through a caustic (Jensen et. al. 2011), as such phase corrections are absent from the measured autoproduct (Worthmann and Dowling 2018). Such processing is possible because the user has full control over how the weight vector is calculated in any MFP scheme. 3. EXPERIMENTAL SETUP & RESULTS The experimental data used in this study was measured during the 2010-2011 Philippine Sea Experiment (PhilSea10) (Worcester et. al 2013). This was an underwater oceanographic - 1011 -
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experiment, and the utility of the dataset for source localization research was unexpected. PhilSea10 included the collection of thousands of transmissions that came from a variety of sources. This study explores the first 120 pings (nominally the first 100 pings plus several others for snapshot averaging purposes) measured from the second closest source positioned 210 km downrange from the receiving array. The dataset used was provided to the authors by P. Worcester and M. Dzieciuch, lead scientists on the experiment, as matched-filtered time series for each hydrophone. This matched-filtering process technically requires knowledge of the source waveform, but it is here used only to achieve high SNR. All signal waveforms are scrambled across frequency by applying a random phase to each frequency bin to prevent source waveform information from reaching the signal processing steps. Additionally, the experimentalists also provided an acoustic sound speed profile measured near the receiving array, as well as nominal receiver positions for 149 elements in the receiving array. All calculations were done using this given information without alteration. Based on this experimental geometry, the performance of conventional MFP and FDSL for both simulated and measured signals was calculated. In simulation, the pressure fields at the array were calculated using Bellhop. For MFP, 21 individual field samples (f = 200 Hz, 205 Hz, … , 300 Hz) from the measured bandwidth were used to construct the ambiguity surface. For FDSL, five difference frequency samples (Δf =1 Hz, 1.25 Hz, … , 2 Hz) were used to construct the ambiguity surface. Fig. 1 shows sample ambiguity surfaces calculated using measured field values for MFP and FDSL. The performance was also calculated for simulated fields, with results being similar to the results given in Fig. 2. In simulation, both
Figure 1: Ambiguity surfaces calculated applying (a) Bartlett MFP, and (b) FDSL to the first valid measurement of the given source during the PhilSea10 exercise. MFP and FDSL successfully localize the relevant sources. However, in measurement, only FDSL successfully localizes the given sources within ±5% in range and depth. It is worth noting that FDSL shows approximately an 8 km range bias. This range error is seen in all localization results, and could possibly be corrected by taking into account bathymetric variations in the range-depth plane defined by the source and the vertical receiving array. To demonstrate the robustness of FDSL to environmental mismatch, both types of processing were performed on the first 100 measured transmissions. The source localization results for both algorithms are shown in Fig. 2, along with a nominal localization success boundary that is an ellipse with axes defined by ±5% of the overall search range and depth (±15 km and ±300 m, respectively). These bounds are chosen to be reasonable and to indicate that the overall results are not obtained by chance. Initially, 97% of the trials are found to be successful for FDSL and 0% for MFP. Therefore, one final elementary signal processing step, snapshot averaging, was taken. Snapshot averaging is here taken to mean that ambiguity surfaces from successive pings were averaged. With two snapshot averaging, 100% localization success is achieved for FDSL. The localization performance of MFP did not improve for any amount of snapshot averaging (up to 20 snapshots).
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Figure 2: Scatter plot of source localization results from the first 100 pings transmitted by a source 210 km from the vertical receiving array during the PhilSea10 experiment. Conventional MFP does not localize the source. FDSL successfully localizes the source 97 times. Given these localization results, the robustness of both conventional MFP and FDSL to elevated noise levels was tested. The noise level was varied here by adding either simulated or measured ocean noise. The simulated noise was created in the time domain by generating random numbers according to normal distributions with zero mean and unit standard deviation. This simulated time series was then Fourier transformed to place it into the frequency domain. The measured noise time series was collected during the experiment immediately before the measured signal was received. From here, either noise sample was multiplied by ď Ą according to (9) in order to give the desired SNR. This definition of SNR, (9) is standard (Jensen et. al. 2011). The simulated performance of conventional MFP and FDSL in the presence of elevated levels of simulated noise is shown in Fig. 3. In addition to this, the
Figure 3: Noise Rejection capabilities of simulated and measured MFP and FDSL for -30<SNR<30. Simulations of both are conducted by using Bellhop to compute a forward field, and simulated noise is added to this field at a given SNR. Measurements of both are computed by adding measured noise to the given measured signal. measured performance of MFP and FDSL in the presence of measured noise is also shown in Fig. 3. Here, success is determined by the percentage of successful localizations based on the success criterion defined in Fig. 2. The simulated results show that MFP has the best noise rejection capability. Despite this, MFP fails to successfully localize a source at any SNR using measured signals. FDSL, while not as robust to simulated noise as is MFP, is capable of - 1013 -
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localizing sources down to SNRs of –8 dB or so in the presence of both simulated and measured noise. 4. SUMMARY AND CONCLUSIONS Frequency-difference source localization (FDSL) is a new below-band technique for source localization in imperfectly known deep ocean environments that is akin to conventional matched field processing (MFP). FDSL successfully localizes a source positioned 210 km downrange from a vertical receiving array in the Philippine Sea using both simulated and measured data. In the chosen difference-frequency range, the performance of FDSL is robust to measured-field-to-calculated-field mismatch that is ubiquitous in long range acoustic propagation in the deep ocean. The noise rejection capabilities of both algorithms were also explored. Using simulated data, MFP rejects noise down to SNRs near – 20 dB without mismatch, but fails to localize the source (at all SNRs) using PhilSea10 experimental data. However, using both simulated and experimental data, FDSL is able to successfully localize the source down to SNRs near –8 dB. Approaches that allow FDSL to inherit the greater noise rejection capabilities of MFP are now being explored. REFERENCES [1] Dowling, D.R. and K. Sabra, Acoustic Remote Sensing, Ann. Rev. Fluid Mech. 47, pp. 221-243, 2015. [2] Bucker, Use of calculated sound fields and matched-field detection to locate sound sources in shallow water. J. Acoust. Soc. Am. 59, pp. 368, 1976. [3] Debever, C, and W.A. Kuperman, Robust matched-field processing using a coherent broadband white noise constraint processore, J. Acoust. Soc. Am. 122, pp. 1979-1986, 2007. [4] Baggeroer, A.B., W.A. Kuperman, and P.N. Mikhailevsky. An overview of matched field methods in ocean acoustics. IEEE J. Ocean Eng. 18, pp. 3018-3029, 1993. [5] Worthmann, B.M., H.C. Song, and D.R. Dowling, High frequency source localization in a shallow ocean using frequency-difference matched field processing. J. Acoust. Soc. Am. 138, pp 3549, 2015. [6] Worthmann, B.M., H.C. Song, and D.R. Dowling, Adaptive frequency-difference matched field processing for high frequency source localization in a noisy shallow ocean. J. Acoust. Soc. Am. 141, pp. 543-556, 2017. [7] Worcester, P.F., M.A. Dzieciuch, J.A. Mercer, R.K. Andrew, B.D. Dushaw, A.B. Baggeroer, K.D. Heaney, G.L. D’Spain, J.A. Colosi, R.A. Stephen, J.N. Kemp, B.M. Howe, L.J. van Uffelen, and K.E. Wage. The North Pacific Acoustic Laboratory deepwater acoustic propagation experiments in the Philippine Sea, J. Acoust. Soc. Am., 134(4), pp. 3359-3375, 2013. [8] Jensen, F.B., W.A. Kuperman, M.B. Porter, and H. Schmidt. Computational Ocean Acoustics, 2nd Ed., AIP, pp. 705-722, 2011. [9] Porter, M.B. and H.P. Bucker. Gaussian beam tracing for computing ocean acoustic fields. J. Acoust. Soc. Am. 82, pp. 1349-1359, 1987. [10] Porter, M. The KRAKEN normal mode program. Defense Technical Information Center (DTIC). [11] Kinsler, L.E., A.R. Frey, A.B. Coppens, J.V. Sanders, Fundamentals of Acoustics, 4th Ed., J. Wiley & Sons, 2000. [12] Worthmann, B.M. and D.R. Dowling. The frequency-difference and frequencysum acoustic-field autoproducts. J. Acoust. Soc. Am. 141, pp 4579, 2017. - 1014 -
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DATA COLLECTION IN SHALLOW FRESH WATER SCENARIOS WITH LOW-COST UNDERWATER ACOUSTIC MODEMS
Filippo Campagnaroa, Fabian Steinmetzb, Alberto Signoria, Davide Zordana, Bernd-Christian Renner b, Michele Zorzia a
Department of Information Engineering, University of Padova, via Gradenigo 6/B, 35131 Padova, Italy b Research Group smartPORT, Hamburg University of Technology, Am Schwarzenberg-Campus 1, 21073 Hamburg, Germany Contact author: Filippo Campagnaro, Department of Information Engineering, University of Padova, via Gradenigo 6/B, 35131 Padova, Italy, email: campagn1@dei.unipd.it, telephone number: +39 049 827 7778. Abstract: The Robotic Vessels as-a-Service (RoboVaaS) project intends to exploit the most advanced communication and marine vehicle technologies to revolutionise shipping and nearshore operations, offering on-demand and cost-effective robotic-aided services. In particular, the RoboVaaS vision includes a ship hull inspection service, a quay walls inspection service, an antigrounding service, and an environmental and bathymetry data collection service. In this paper, we present a study of the underwater environmental data collection service, performed by a low-cost autonomous vehicle equipped with a very low-cost acoustic modem and moving within a network of submerged acoustic sensor nodes. To this end, an underwater acoustic network composed by both static and moving nodes has been implemented and simulated with the DESERT Underwater Framework, where the performance of the smartPORT Acoustic Underwater Modem have been mapped in the form of lookup tables. The performance of the modem has been measured near the Port of Hamburg, where the RoboVaaS concept will be demonstrated with a real field evaluation. Keywords: Underwater acoustic network simulations, DESERT Underwater, AHOI modem. 1 INTRODUCTION Today, 90% of transported goods are waterborne [1]: the Robotic Vessels as-a-Service (RoboVaaS) project [2] intends to reorganize this sector by exploiting the most advanced communication technologies to interconnect unmanned vessels (UVs), such as autonomous surface vessels (ASVs), and autonomous underwater vehicles (AUVs). The main goal of this project is to offer on-demand robotic-aided services, requiring the capabilities of both Internet of Things (IoT) and Internet of Underwater Things (IoUT) [3,4] devices. The RoboVaas project - 281 -
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intends to identify those services, beneficial for near-shore maritime operations, that have a strong positive impact on automation and digitization. These include, among others, a data collection service with a wide range of applications, such as measuring environmental data with an underwater sensor network. In this paper we refer to this service, which requires the deployment of a low-cost underwater sensor network (UWSN). In this application, an AUV patrols the area and collects data from the UWSN, equipped with a low cost underwater acoustic modem, the smartPORT Acoustic Underwater Modem (AHOI) [5] (presented in Section 2). In order to retrieve the data from such network, we design a polling-based Medium Access Control (MAC) protocol (Section 3), tailored to the needs of an UWSN [6]. In Section 4, we evaluate the proposed solution via simulations based on field measurements, and, finally, in Section 5, we draw our concluding remarks. 2 SMARTPORT ACOUSTIC UNDERWATER MODEM This section presents the smartPORT Acoustic Underwater Modem (AHOI) [5]. The AHOI modem is a small, low-power and low-cost acoustic underwater modem (see Fig. 1), developed to be integrated into micro AUVs or UWSNs. The modem consists of three stacked Printed Circuit Boards (PCB) with an overall size of 50x50x25 mm³ and a component cost of about €200. The first PCB includes a CortexM4 microcontroller, power supply and external connections. In addition, the second PCB works as the receiver and involves amplifiers, a bandpass filter and an analog-to-digital converter. To receive acoustic signals with different signal strength, the amplifier gains are software adjustable (overall amplification between 4096 dB). In the default setup, a software-based automatic gain control is used to adjust the amplifier gains. Finally, the third PCB is the transmission board including a digital-to-analog converter and a power amplifier. The power consumption in idle and receive mode is around 300 mW and 2.1 W during data transmission with highest amplification. For the acoustic signal reception and transmission, the AHOI modem uses an Aquarian Scientific AS-1 broadband measurement hydrophone with a price of about €400. In the case of the highest power amplifier level and a transmission in the 50-75 kHz range, the transmission source level is between 150160 dBre1µPa²@1m with an AS-1 hydrophone.
Fig. 1: AHOI acoustic underwater modem with hydrophone. Signal processing is realized in software on the microcontroller, which allows a fast reconfiguration of frequency and coding setups. In the default setup, the modem uses an orthogonal binary frequency shift keying (BFSK) with 2.56 ms symbol duration and a 781.25 kHz frequency spacing. Each symbol consists of four superimposed sinusoidal waveforms. To counter frequency cancellations caused by multi-path propagation, each bit is repeated on three different carriers, and frequency hopping (FHSS) is applied to avoid inter-symbol interference. All in all, the modem has 25 kHz of bandwidth around a central frequency of 62.5 kHz. The default setup in combination with a Hamming coding leads to a net data rate of 260 bps (up to 4.7 kbps are feasible).
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2.1 Modem’s performance in very shallow water A real-world evaluation was performed in a marina in southern Hamburg. The marina is located in a branch of the river Elbe, where depth ranges from 3.5 m to 7.5 m depending on the tide. The experiments were performed on Nov. 11, 2018. In total six AHOI modems were deployed on a jetty, one modem acted as the sender and the other five as the receivers. All hydrophones were submerged 1.5 m under the water surface and each modem was connected to a laptop to record the received packets. During the evaluation, packets with different payload lengths were transmitted. In each setup the sender sent 200 packets with randomly generated payload. Table 1 contains the packet delivery rate (PDR) of the five receivers for 16, 32, and 96 bytes payloads. In this test, the node deployed 99 m far from the transmitter received more packets than the node at 24 m due to the effects of multipath and automatic gain control (the receiver at 99 m used a higher gain than the receiver at 24 m, as the latter had to employ a low gain to avoid saturation). Size [Bytes] 16 32 96
PDR@12 m 96.5% 95.0% 77.0%
PDR@24 m 75.0% 70.0% 92.0%
PDR@46 m 89.5% 88.5% 83.0%
PDR@99 m 88.0% 99.0% 100.0%
PDR@152 m 84.5% 51.5% n.a.
Table 1: Packet delivery rate achieved during the real-world evaluation in a marina in Hamburg. 2.2 Modem’s reaction to shipping noise The typical range of applications of UWSN include marinas, ports and rivers with high shipping activity. Each vessel produces acoustic noise, caused by propellers, machineries and other effects [7]. For this reason, in order to provide a stable communication link, the AHOI modem must be robust against vessel noise. The acoustic noise emitted by ships and vessels has highest power spectral density (PSD) below 10 kHz, which is smaller than the communication frequency range. In addition, the AHOI modem receiver involves a bandpass filter between 50 kHz and 75 kHz. Furthermore, a simulation was performed to prove the stability against vessel noise. The noise was generated offline and added to different recorded packets. A single AHOI modem was used to receive the signal, which was generated with an arbitrary signal generator (TiePie Handyscope HS5, 200 kHz sampling). The signal generator simulated the received hydrophone voltage for different PSDs. During the simulations a constant receiving sensitivity of -208 dBV re 1 ¾Pa for frequencies up to 100 kHz was assumed. Fig. 2 shows the PSDs of received packets at the receiver side. At first, the packets were transmitted without additional vessel noise. In the following simulations, different noise profiles were added to the packets.
Fig. 2: Power spectral density at the receiver side.
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The noise profiles were generated according to [7] and are shown in Fig. 2. For each combination of vessel noise level and communication signal strength, 100 packets (with 32 B payload) were transmitted. In all cases, the AHOI modem received all packets. 2.3 Modem’s reaction to interference In addition to the vessel noise discussed in Section 2.2, other underwater modems in a network could disturb the transmission. To evaluate the effect of packet interference and the resulting PDR, the described hardware setup in Section 2.2 was used. Instead of additional simulated vessel noise, a second recorded packet was added to the generator samples. All packets carried 32B payload and had a signal duration of 1.311 s, including the synchronization symbols. Fig. 2 displays PSDs of the different transmitted packets. Three different interference situations were simulated, for different degrees of overlap between the intended packet and the interfering packet, based on the time displacement between the two: (i) 0 s delay (100% overlap), (ii) 0.5 s delay (61.9% overlap), and (iii) 1 s delay (23.7% overlap). In addition, reception without any interference was also simulated (0% overlap). For each combination of communication signal strength and delay, 100 packets were transmitted. The PDR has been measured by varying the interference component and its performance mapped in the simulator. Specifically, the Signal to interference Ratio (SIR) component has been calculated by employing the mean power model, computed as SIR P r Pi ovr , where Pr is the power received by the signal carrying the data packet, Pi the power of the interfering signal and ovr the overlap percentage of the two signals, calculated as the portion of time the two signals are interfering divided by the total packet duration. The measurements are reported in Table 2. Rx power [dB re Pa2@1m] 127.8, 121.8, 118.3, 115.8 121.8 127.8, 121.8, 118.3, 115.8 118.3
PDR
average PDR
100.0%
SIR [dB] (mean power) 0
0%
0.0%
127.8 Same as Rx power
23.7% 61.9%
0.248 2.086
0.0% 33.5%
115.8
100%
≥2.5
0% 12%, 36%, 46%, 40% 100%
Interf Rx power [dB re Pa2@1m] Same as Rx power
Overlap
100%
Table 2: AHOI modem’s reaction to interference. In order to extend the measurements and model them in the simulator, we interpolated the samples by employing the Piecewise Cubic Hermite Interpolating Polynomial (PCHIP) with Matlab, obtaining the line depicted in Fig. 3.
Fig. 3: Packet delivery ratio vs Signal to Interference Ratio, with PCHIP interpolation. - 284 -
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The AHOI modem performance figures have been included in the form of lookup-tables (LUTs). In particular, the PDR vs distance has been mapped to obtain the modem’s behaviour in case of absence of interference. In addition, the PDR vs SIR LUT sampled by the PCHIP interpolation line presented in Fig. 3 has been included in the simulator, to obtain the penalty introduced by the interference. The resulting packet delivery ratio is approximated as PDR PDR d PDR SIR , where PDR d is the packet delivery ratio obtained at a distance d, while PDR SIR is the packet delivery ratio obtained in ideal conditions with signal to interference ratio equals to SIR . 3 POLLING MAC PROTOCOL Both [8] and [9] demonstrated that a polling-based MAC layer suits well underwater datamuling scenarios: in the uwpolling protocol [8], the AUV first triggers the channel and then waits for the static nodes to answer with a probe, that contains information on how much data the static node needs to transmit to the AUV. Before probing the channel, the static nodes wait for a back-off time randomly generated within a maximum interval TbMax. Then, the AUV sends a poll packet to assigns each static node the time interval within which that node can transmit its own data packets. In [9] the authors demonstrated that if the propagation delay is bigger than the packet duration and the distances from the AUV to the static nodes span a wide range, TbMax can be set to 0 to maximize the throughput, without increasing the probability of packet collisions. This result is not applicable in our scenario, where the packet duration can be bigger than the propagation delay, since many nodes are deployed within a range of 100 m, and the modem bit rate is 260 bps. The protocol employed in our simulations is an enhanced version of uwpolling. In this protocol, the AUV first wakes up the surrounding nodes with a Trigger packet (TrP). Then each node i that received TrP and has data to transmit sends back a Probe packet (PrP) to the AUV containing the number of packets it needs to transmit ( P ). The transmission of PrPs is contention-based: each node waits for a random back-off period, up to a Maximum Backoff Time (TbMax), before transmitting its own PrP. After this first phase, the AUV selects the nodes to be polled in order, according to a fair policy: for each node i that successfully probed the channel, the AUV computes the weight wgt i P EXP , i P RX , i , with P equal to the total number of packets received by the AUV from node i at that moment, and orders the nodes according to wgt. If no PrP has been received by the AUV within a Maximum Trigger Timeout (MTT) bigger than TbMax + RTT, or after the AUV finishes to poll all nodes of the list, the AUV transmits a new TrP. The uwpolling protocol provides also the possibility to use a sink node different from the AUV, to which to upload the data collected by the AUV [10]. To use this feature with the AHOI modem, data compression techniques should be investigated in order to forward all the data collected from the AUV when the sink is in the range of the vehicle. This solution will be part of our future work. EXP
,i
RX , i
4 SIMULATIONS In Section 4.1 we describe the simulation settings and the simulation scenario, while in Section 4.2 we present the simulation results.
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4.1 Simulation settings and scenario description In our scenario the water conditions are the same as in the experiment presented in Section 2.1: the water column between 3 and 7 meters deep, and the nodes are deployed 1.5 meters below the sea surface. Each node is equipped with an AHOI modem with carrier frequency 65.2 kHz, bandwidth 25 kHz, payload data rate (computed as the packet size, in bits, divided by the total packet duration, including the preamble, in seconds) of 200 bps, and transmission power 156 dB re Pa2@1m. The packet size is set to 256 bits, and each polled node transmits 5 data packets. The protocol is evaluated in different working conditions, where the AUV speed (v) is varied from 0.1 m/s to 4 m/s, and the node density (N), defined as the number of nodes within the coverage area of the AUV (circular area, with a radius of 110 meters), is varied from 3 to 30 nodes. A realization of the scenario with N = 6 is depicted in Fig. 4. The nodes position (blue dots) is set randomly, along the AUV path (red line). The AUV performs 7 laps of the same path, 1.6 km long, moving at a constant speed during each simulation run. The system is simulated with the DESERT Underwater framework.
Fig. 4: Network scenario. The AUV path is described by the solid red line. The solid green lines define the area in which nodes are randomly placed 4.2 Simulation results In this section we present the simulation results obtained by varying the node density and the AUV speed. The results are obtained averaging over 20 independent simulation runs. All the simulations are performed considering that each node always transmits 5 data packets when it is polled by the AUV. The analysed performance metrics are the throughput (THR) and Jain’s Fairness Index (JFI), computed as n
THR
i
x ( i ) sz T
1
,
and
JFI
n
i
x (i)
2
n
n
i
x (i)
2
1
,
(1)
where x(i) is the number of packets received by the AUV from node i, n is the total number of nodes in the whole network, sz is the packet size, in bits, and T is the total simulation time, in seconds. In Fig. 5 (a) the overall throughput of the network is reported as a function of the maximum backoff time TbMax for different values of the node density N. For N = 6, the throughput first increases as TbMax increases by reaching a maximum peak value of 58 bps for TbMax = TbOp(6)=8 s. With TbMax lower than 8 s the collision of probe packets at the AUV is very likely, thus many probe packets will not be received, and in turn a lower number of nodes will - 286 -
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be polled in the subsequent polling round. When TbMax is greater than 8 s the throughput starts to decrease as well, since some of the triggered nodes may get out of the transmission range of the AUV after waiting TbMax seconds. A similar behavior occurs also for N = 3 and N = 10, where TbOp(3) = 6 s and TbOp(10) = 14 s, respectively. For N = 30, TbOp(30) = 28 s, but the maximum throughput becomes 16% lower than with N = 6, because 28 s is enough to avoid collisions, but in that amount of time the AUV already moved 56 m away from the position in which it sent the trigger, leading to a large number of triggered nodes out of range. With N = 3, N = 6 and N = 10, a TbMax = TbOp(N) provides a JFI of 0.89, 0.85 and 0.8, respectively, that is close to the maximum JFI reachable with those densities (Fig. 5 (b)). Therefore, in these cases the choice TbMax = TbOp(N) provides a good fairness as well. On the other hand, with N = 30, the AUV is not able to serve in a fair way all the nodes in the network, indeed the maximum JFI value obtained is equal to 0.58.
Fig. 5: Throughput of the network (a) and JFI (b) vs backoff time, for different values of the node density N.
Fig. 6: Throughput of the network (a) and JFI (b) vs backoff time, for different values of the AUV speed v. Fig. 6 (a) depicts the overall throughput of the network as a function of the maximum backoff time with N = 6, by varying the AUV speed v between 0.1 m/s and 4 m/s. The peak for - 287 -
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the overall throughput is reached, on average, around TbMax = TbV = 10 s, with small differences in the peak positions when varying the AUV speed. When TbMax=TbV, JFI is almost maximized for v 2 m/s (see Fig. 6 (b)). On the other hand, with a speed of 4 m/s, the nodes are not served in a fair way because the AUV moves too fast, and many triggered nodes become out of range due to the new AUV position. 5 CONCLUSIONS In this paper, we proved the feasibility of a data-muling application with the very low-cost AHOI modem, via simulations based on real field measurements. The proposed polling protocol ensures fairness to all nodes for AUV speeds up to 2 m/s. This encourages us tu pursue further developments, aimed at the optimization of the protocol and at the evaluation of the system in a real field test. ACKNOWLEDGEMENTS This work has been supported in part by the Italian Ministry of Education, University and Research (MIUR), the German Federal Ministry for Economic Affairs and Energy (BMWi, FKZ 03SX463C), and ERA-NET Cofound MarTERA (contract 728053). REFERENCES [1] Inernational Chamber of Shipping, “Key Facts,” Last time accessed: April 2019. [Online]. Available: http://www.ics-shipping.org/shipping-facts/key-facts [2] RoboVaaS - Robotic Vessels as-a-Service, Last time accessed: April 2019. [Online]. Available: https://www.martera.eu/projects/robovaas, [3] R. Guida, E. Demirors, N. Dave, J. Rodowicz, and T. Melodia, “An Acoustically Powered Battery-less Internet of Underwater Things Platform”, UComms, Lerici, Italy, 2018 [4] “SUNRISE: Building the internet of underwater things.” Last time accessed: May 2019. [Online]. Available: http://fp7-sunrise.eu/ [5] C. Renner and A. J. Golkowski, “Acoustic Modem for Micro AUVs: Design and Practical Evaluation,” in Proc. of the 11th ACM International Conf. on Underwater Networks & Systems (WUWNet), Shanghai, China, Oct. 2016 [6] C. Audoly and E. Rizzuto, “Ship underwater radiated noise patterns,” AQUO Project Consortium, final report R2.9, 06 2015. [7] F. Favaro, P. Casari, F. Guerra, and M. Zorzi, “Data upload from a static underwater network to an AUV: Polling or random access?” in Proc. MTS/IEEE Oceans, Yeosu, Republic of Korea, May 2012. [8] W. Liu, J. Weaver, L. Weaver, T. Whelan, R. Bagrodia, P. A. Forero, and J. Chavez, “Adaptive Polling for Underwater Data Collection Systems”, MTS/IEEE Oceans, Charleston, US, October 2018 [9] J. Heidemann, M. Stojanovic, and M. Zorzi, “Underwater sensor networks: applications, advances and challenges”, Phil. Trans. of the Royal Society 2012, vol. 370, no. 1958, pp 158-175 [10] F. Campagnaro, D. Zordan, A. Signori, F. Favaro, and M. Zorzi, “Underwater Acoustic Sensors Data Collection in the Robotic Vessel as-a-Service Project”, in Proc. MTS/IEEE Oceans, Marseille, France, June 2019
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AN EFFICIENT MODEL FOR OFFSHORE PILE DRIVING NOISE TAKING INTO ACCOUNT THE HAMMER CHARACTERISTICS
Elin Klages, Jonas von Pein, Stephan Lippert, Otto von Estorff Hamburg University of Technology, Institute of Modelling and Computation, 21073 Hamburg, Germany, e-mail: mub@tuhh.de Abstract: Pile driving of state-of-the-art piles for offshore wind farms requires the application of noise mitigation systems to assure that sound pressure levels fulfill official regulations. Currently applied are mainly sound mitigation systems reducing the propagation of the emitted sound, e.g., bubble curtains, rather than reducing the sound generation itself. However, rapidly increasing dimensions of wind turbines with even higher pile diameters demand additional measures to keep sound pressure levels within the defined limits. Therefore, the design of the hammer regarding its acoustic characteristics has recently gained attention. To optimize the hammer design, it is required to model the underwater sound pressure caused by the hammer impact. Here, the computation time is crucial to the overall optimization time. However, currently applied models to estimate the underwater sound pressure are either computationally expensive (finite element models) or do not allow for a detailed hammer design (analytical models). Within this contribution, a computationally low-cost model, which is able to take modifications of the hammer into account, is presented. The model consists of two steps: In the first step, a finite element model is applied to compute the pile head acceleration. In the second step, a transfer function is used to obtain the sound pressure level based on the pile head acceleration.
Keywords: Offshore pile driving, underwater noise, impact-hammer design, computationally low-cost model
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1. INTRODUCTION Offshore wind energy is an important source of renewable energy. However, every construction of a new offshore wind park causes high underwater noise levels, threatening to harm marine mammals. Especially crucial is the piling of commonly applied monopiles that are driven into the sea bed using an impact hammer. In order to protect the marine fauna, several countries have defined official limits for sound pressure levels during pile driving. Noise mitigations systems are usually applied to assure that sound pressure levels do not exceed these limits. However, the capacity of offshore wind turbines is increasing and so are pile diameters. To drive larger piles, more energy is required, thus more noise is emitted and sound mitigation measures at the pile driving location have to be extended. In view of this development, the modification of the sound source itself, i.e. hammer and pile, in order to decrease sound emission, has recently gained attention. In order to design a hammer with improved acoustic characteristics, it is necessary to be able to estimate the sound pressure levels accurately. Several approaches, to model sound emission and propagation caused by offshore pile driving, exist. An overview can be found for example in [1]. In the context of hammer optimization, it is especially important that the chosen modelling approach includes the detailed hammer design. Furthermore, the calculation of the sound pressure levels has to be fast, to be suitable for an optimization. Considering these requirements, neither analytical models nor a detailed Finite-Element (FE) analysis can be applied here: While analytical models do not allow for a detailed consideration of small changes of the excitation signal, FE models for pile driving noise are simply too time-consuming to be used for multiple computations within an optimization. The present contribution aims to present an alternative model that is both, accurate and fast. The proposed model consists of two steps. The first step constitutes a FE analysis of the short hammer impact. In the second step, results of the FE analysis are applied to estimate sound pressure levels using a transfer function (TF) in the frequency domain. 2. ACOUSTIC OFFSHORE PILE DRIVING MODEL To simulate the sound generation, emission, and propagation, a two-step model is applied. First, a FE model of hammer and pile is used to evaluate the hammer impact. The axial pile head velocity is then passed to the second model, used to obtain the underwater sound pressure caused by the vibrations of the pile. The second model may be a detailed FE model of the pile, water and sea bed or a TF based on the FE model. The modelling process is shown in Fig. 1. All three models will be shortly introduced in the following sections.
Fig. 1: Two steps to model the sound generation, emission and propagation caused by offshore pile driving. For the second step, either the FE model or the TF may be applied. Separating the analysis of the impact and the sound propagation into two steps, is a common approach, as used in different ways for example in [2], [3]. In the latter, also transfer functions were discussed. However, in contrast, the present contribution focuses on replacing the second FE model, i.e., the actual acoustic model, with the TF to reduce simulation time during an optimization of the impact hammer.
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2.1 FE model for hammer and pile The first FE model serves to compute the pile head velocity caused by the hammer impact based on the hammer design. It includes only the main hammer components, anvil and impact weight, and the pile. A simplified illustration of exemplary hammer components can be found later on in Fig 3. The influence of water and sea bed on the pile head deformations are negligible and therefore not included in the model. During pile driving, the anvil rests on the pile head and the impact weight is lifted up to fall afterwards on the anvil which transmits the impact to the pile. This process is repeated several hundred times for each pile, however, only one isolated impulse is modeled. For the purpose of modelling, the ram energy is defined via the initial velocity of the impact weight, here set to 1000kJ. 2.2 FE model for the acoustic propagation The second FE model serves to compute the acoustic pressure based on the pile head velocity. It includes the pile, water, and the sea bed. The sea bed is modelled with layers with different sound velocities and densities, originally provided in the context of the project BORA for the offshore wind park BARD Offshore I. Further specifications of both FE models can be found in [4], [5]. 2.3 Transfer function Here, we assume that the dependency of sound emission on pile head deformation is at least approximately linear considering its representation in the frequency domain. The proposed TF relates the underwater sound pressure đ?‘? at one specific position to the axial pile head acceleration đ?‘Žđ?‘? obtained from the FE hammer pile model, i.e. đ?‘‡đ??š(đ?‘&#x;, đ?‘§, đ?‘“) =
đ?‘?(đ?‘&#x;,đ?‘§,đ?‘“) , đ?‘Žđ?‘? (đ?‘“)
(1)
Here đ?‘&#x; refers to the distance to the pile, đ?‘Ś to the đ?‘§ to the height over the sea bed, and đ?‘“ to the frequency. To define the TF, the sound pressure values for at least one hammer design have to be known. Hence, although the TF is here presented as an alternative to the acoustic FE model, it is always based on the same and can therefore never fully replace it. The ram impulse with 1000kJ ram energy of the existing hammer MHU3500S, produced by MENCK, was simulated to obtain reference pressure values to define the TF. 3. COMPARISON OF MODELS In order to discuss the accuracy of the proposed model, two case studies are presented in the following. In both cases, several simulations of the sound emission due to the hammer impact for different hammer designs were performed using the acoustic FE model and the TF to estimate the underwater sound pressure. The difference of the resulting sound pressure levels, ΔSEL and ΔSPLPeak , is then used to evaluate the performance of the TF. For both case studies, a cylindrical pile with 70m length, 6.5m diameter and 80mm wall thickness serves as an example. The embedded length of the pile is 35m and the water depth is - 641 -
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30m. The TF was evaluated in 200m distance to the pile and 2m above the sea bed. The two following sound pressure levels were applied: The sound exposure level (SEL) defined as 1
đ?‘‡ đ?‘?(đ?‘Ą)2
SEL = 10 log10 ( âˆŤ0 đ?‘‡
đ?‘?02
(2)
đ?‘‘đ?‘Ą) [đ?‘‘đ??ľ]
and the peak sound pressure level (SPLPeak ) defined as SPLPeak = 20 log10 (
max(|đ?‘?(đ?‘Ą)|) ) [đ?‘‘đ??ľ], đ?‘?0
(3)
where đ?‘?0 is the reference pressure of đ?‘?0 = 1ÎźPa and đ?‘‡ is the reference time of 1s. 3.1 Case study: Variation of the material of the hammer components The first case study includes 300 variations of the material of the hammer components, i.e. the density đ?œŒ and Young’s modulus đ??¸ of both hammer components. Every hammer design was thus defined via the parameter vector: đ?œƒ = [đ?œŒđ??´đ?‘›đ?‘Łđ?‘–đ?‘™ , đ??¸đ??´đ?‘›đ?‘Łđ?‘–đ?‘™ , đ?œŒđ?‘Šđ?‘’đ?‘–đ?‘”â„Žđ?‘Ą , đ??¸đ?‘Šđ?‘’đ?‘–đ?‘”â„Žđ?‘Ą ]. The samples were randomly distributed and the parameter space was restricted via đ?œŒđ?‘šđ?‘–đ?‘› = 0.5đ?œŒ0 , đ?œŒđ?‘šđ?‘Žđ?‘Ľ = 1.5đ?œŒ0 and đ??¸đ?‘šđ?‘–đ?‘› = 0.5đ??¸0 , đ??¸đ?‘šđ?‘Žđ?‘Ľ = 1.5đ??¸0 . The shape and the original material values đ?œŒ0 , đ??¸0 of the hammer MHU3500S were used. The distribution of the error of the TF is shown in Fig. 2. The results show good agreement of both models. Even the largest error of the SEL is smaller than 0.5dB. Errors in the SPLPeak are larger, up to 2.1dB, but still small considering the difficulty in predicting the SPLPeak . However, since the shape of the hammer components in the case study was the same as used to define the TF, the error might be unrealistically small for other hammer designs.
Fig. 2: Error of the TF for the material parameter study: đ?›Ľđ?‘†đ??¸đ??ż = đ?‘†đ??¸đ??ż đ??šđ??¸đ?‘€ - đ?‘†đ??¸đ??żđ?‘‡đ??š đ?›Ľđ?‘†đ?‘ƒđ??żđ?‘ƒđ?‘’đ?‘Žđ?‘˜ = đ?‘†đ?‘ƒđ??żđ?‘ƒđ?‘’đ?‘Žđ?‘˜,đ??šđ??¸đ?‘€ - đ?‘†đ?‘ƒđ??ż đ?‘ƒđ?‘’đ?‘Žđ?‘˜,đ?‘‡đ??š 3.2 Case study: Variation of the shape of the hammer components In contrast to the previously presented case study, this section discusses the performance of the TF for different shapes of the hammer components. For this purpose, the cross section of the shape of the hammer components was defined via a polygon. A visualization of exemplary hammer components defined as polygons is shown in Fig. 3. Axis symmetry was assumed. The shape was described by 13 parameters. 198 samples were randomly distributed. The parameter
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space was restricted by the maximum height ℎ and diameter � for each component, i.e. ℎ����� = 2�, ������ = 8� and ℎ����ℎ� = 15�, �����ℎ� = 4�. The corresponding differences in sound pressure levels are shown in Fig. 4.
Fig. 3 A simplified illustration of the axis symmetric original hammer components at the left side and one example of the polygon parameterization at the right side. The pile is here approximated as a hollow cylinder.
Fig. 4: Error of the TF for the parameter study of the shape of the hammer components: đ?›Ľđ?‘†đ??¸đ??ż = đ?‘†đ??¸đ??ż đ??šđ??¸đ?‘€ - đ?‘†đ??¸đ??ż đ?‘‡đ??š đ?›Ľđ?‘†đ?‘ƒđ??żđ?‘ƒđ?‘’đ?‘Žđ?‘˜ = đ?‘†đ?‘ƒđ??żđ?‘ƒđ?‘’đ?‘Žđ?‘˜,đ??šđ??¸đ?‘€ - đ?‘†đ?‘ƒđ??ż đ?‘ƒđ?‘’đ?‘Žđ?‘˜,đ?‘‡đ??š . Red bars refer to the error distribution, considering only hammers with an efficiency Îź of at least 0.8. In comparison to the previous case study, errors are larger, but still indicate general good agreement of the TF and FE model as no extreme outliers occurred. If only hammers with a driving efficiency of at least 0.8 are considered, the maximum error is even less than 2dB for both sound pressure levels, the SEL and the SPLPeak . However, even deviations of only 2dB might hinder an optimization, if it affects the comparison of the acoustic characteristics of hammer designs.
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4. CONCLUSIONS The estimation of sound pressure levels based on a linear TF in the frequency domain were discussed. The use of the TF for two exemplary case studies showed overall promising results and therefore support the approach of a linear TF in the frequency domain. However, the results of the second case study indicate that the here applied reference pressure based on an existing hammer might cause larger errors if the hammer of choice is too different from the original one. Future work will be directed to model the sound emission and propagation in the frequency domain to obtain a more accurate TF. 5. ACKNOWLEDGEMENTS The presented research is part of the collaborative research project “Investigation of primary measures for the reduction of underwater noise emission during offshore pile driving” involving the companies Novicos GmbH and MENCK GmbH as well as the Hamburg University of Technology. The authors gratefully acknowledge the project funding by the Federal Ministry for Economic Affairs and Energy due to an act of the German Parliament (Project No. 0324262). REFERENCES [1] Stephan Lippert et al., COMPILE – A generic benchmark case for predictions of marine pile-driving noise, IEE Journal of oceanic engineering, Vol. 41, pp. 1061 – 1071, 2016. [2] Per G. Reinhall, Peter H. Dahl, Underwater Mach wave radiation from impact pile driving: Theory and observation, The Journal of the Acoustical Society of America, Vol. 130, pp. 1209 – 3011, 2011. [3] Mario Zampolli et al, Validation of finite element computations for the quantitative prediction of underwater noise from impact pile driving, The Journal of the Acoustical Society of America, Vol. 133, pp. 72 - 81, 2013. [4] Kristof Heitmann, Vorhersage des Unterwasserschalls bei Offshore-Rammarbeiten unter Berücksichtigung von Schallminderungsmaßnahmen, Shaker Verlag, 2017. [5] Kristof Heitmann et al., Numerical determination of equivalent damping parameters for a finite element modelt o predict the underwater noise due to offshore pile driving, In Euronoise 2015, Maastricht, Netherlands, pp. 605 – 610, 2015.
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VARIABILITY OF THE SOUND FIELD IN THE PRESENCE OF INTERNAL KELVIN WAVES IN A STRATIFIED LAKE
Uzhansky E.1, Katsnelson B.1, Lunkov A.2, Ostrovsky I.3 1
L. Charney School of Marine Sciences, University of Haifa, Israel, 3498838 Haifa, Prokhorov General Physics Institute of the Russian Academy of Sciences, 38 Vavilova str. Moscow, 119991, Russia, 3 Israel Oceanographic & Limnological Research, Yigal Alon Kinneret Limnological Lab., P.O. Box 447, Migdal 1495001, Israel 2
Contact author: Ernst Uzhansky Room 270, Multi-Purpose Blvd., University of Haifa, 199 Aba Khoushy Ave., Mount Carmel, Haifa 3498839, Israel e-mail: ernstuzhansky@gmail.com Abstract: The spatiotemporal variability of low- and mid-frequency sound field in the presence of internal Kelvin waves (IKWs) was studied in Lake Kinneret (Israel). Experimental measurements of the sound field were carried out using a vertical line array (VLA) consisting of ten hydrophones with 3 m spacing. The VLA was deployed in the deepest (37 m) part of the lake. Signals were transmitted from the source deployed at peripheral lake location at the distance of 5.5 km from the VLA at 5-m depth. Chirp signals (300 – 2000 Hz) were transmitted with 5 sec intervals during >24 hours (the period of the IKWs). IKWs were registered using three thermistor chains (TCs) positioned along the northwest transect at 10-m, 20-m and 37m station depths. This setting allowed us to characterize the variations of thermal structure and the corresponding sound speed profile along the transect. The vertical structure of sound field obtained with the VLA shows correlation with temporal variability due to IKWs. The modeling of sound propagation was done using Parabolic Equation (PE) method, taking into account the parameters of bottom, lake bathymetry and variation of water temperature. The PE results showed close agreement with experimental measurements. [This work was supported by Israel Science Foundation, grant 565/15].
Keywords: Internal Waves, underwater acoustics.
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1. INTRODUCTION Internal waves (IWs) populate various stratified aquatic systems from small reservoirs, such as lakes to the ocean. Water motions caused by IWs lead to an increase in turbulence and diapycnal mixing at the peripheral lake zones, especially in areas where the metalimnion (the thermocline) touches the bottom [1]. It results in enhanced vertical transport of solutes from the nutrient-rich hypolimnion (water below the thermocline) to the upper productive water layer, leading to increased primary productivity rates [2]. From acoustical point of view, IWs are one of the most important factors influencing sound propagation in the ocean. The acoustic effect of IWs in negligible at very short ranges but becomes important for path lengths of several kilometers [3]. The capability of acoustical methods in studying IW dynamics was demonstrated on the shelf zone of the ocean [4], while no such measurements were done in lakes. The presence of a sharp thermocline and respective gradient in water density in the metalimnion are important for the development of various types of Internal Waves (e.g. Kelvin waves, Poincare waves, high-frequency gravitational waves) e.g. [5], [6]. Rotational internal Kelvin waves (IKWs) showed the most significant contribution to the internal waves spectra [7]. It was shown in [8], remarkable influence of IKWs on the sound propagation and some specific features of variation of interference pattern of the sound field. 2. MESUREMENTS AND INSTRUMENTAION The study is carried out in a warm monomictic Lake Kinneret. It possesses typical shallow water waveguide properties (depth of ≈10 to 40 m, sound speed profile with a slight negative gradient). The lake bottom is represented as a less than 1 m thick gas-rich layer with low compressional wave velocity (≈300-500 m s-1) lying above gas-free thick bottom with relatively high sound speed (1550 m s-1). Parameters of the bottom were estimated from direct sediment sampling and acoustical sediment characterization [9]. During the winter temperature of the whole water body is about 16° C, while during the summer lake is strongly stratified. In the following work we consider sound field propagation along the acoustic track (Fig. 1) from the source deployed at 5 m depth at Station T (7.5 m bottom depth) to the receiving system, i.e. Vertical Line Array of 10 hydrophones with 3 m spacing positioned at Stn. A (36 m bottom depth). Experiment The experiment was carried out at the end of June 2018 when sharp thermocline is presented (Fig. 2). Linear-frequency modulation pulses (Chirp) at 300 – 2000 Hz frequency band with 5 sec duration were transmitted for > 24 hours (the period of the IKW). Each cycle of 10 consequent pulses was repeated every 30 minutes from 17:45 June 26 till 22:00 June 27. No pulses were radiated from 03:30 till 11:45 on June 27. The recorded signals were crosscorrelated with the radiated signals to obtain pulse response. The Hilbert transform was applied to extract the experimental pulse envelopes. Three thermistor chains were deployed at three stations (H, F, A) to register temporal evolution of the water temperature, and thus, temporal variability of the IKW.
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Fig. 2: Bathymetric map of Lake Kinneret and layout of the experiment. A – position of VLA. H, F, A – positions of thermistor strings. T denotes position of the sound source. Simulation Numerical modeling of the sound field was carried out for various thermocline positions for the full cycle of the IKW (i.e. 24 hrs). Temporal variability of the IKW was obtained from temperature data from thermistors chains. Sound field simulations were done with a Parabolic Equation approach using RAMs (M. Collins/NRL). Variation of bathymetry and layered structure of the bottom was considered as stationary (unperturbed) parameters. As a nonstationary (perturbed) component only water temperature variations were used. In our model we consider circular shape of the lake (dashed circle, Fig. 1) with radius TA, spatiotemporal variations of water temperature are similar for each radius of this circle (i.e. temperature variations at transect TA are the same as on the transect HA and have the corresponding shift in time). Thermistor chains were deployed at the northwest transect, whereas acoustic track was positioned at the southwest. Considering period of the IKW 24 hours corresponding to 360º of phase shift, and the angle between these transects α, variations of the IKW at southwest transect were obtained from the +5 hours (i.e. 75º) phase shift of IKW from temperature sensors deployed at Stns. H, F, A. 3. RESULTS Both simulated and experimental sound fields show similar behavior. First, sound field at the VLA is concentrated below the thermocline. Secondly, variability of sound field intensity is connected with vertical thermocline displacements. When thermocline is high, sound field is stretched and less intense. When thermocline is low, it presses the sound field down to the bottom, resulting in higher intensity of the sound field (Fig.3). Analysis of numerical modeling (Fig. 4) shows up to ≈1 km horizontal displacement of the sound field in vicinity to the VLA.
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Fig.2: a) – temperature profile at Stn. A at the day of the experiment (26 June 2018) with CTD; b) – temperature profiles obtained at St. F in June 2017 with a thermistors chain.
Thermocline
Fig.3: a) Transmission Loss at the VLA simulated with RAMs at frequency of 1 kHz, b) experimental sound intensity recorded on the VLA. White color depicts absence of data. Thermocline boundaries are depicted as dashed lines. In the center of is depicted typical thermocline curve.
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Fig4. Numerical model of Transmission Loss along the transect TA at four moments of time. Solid lines represent temperature profiles obtained with thermistors chain at Stations H, F, and A and shifted in time by +5 hrs. Sound source is depicted as a red circle
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4. ACKNOWLEDGEMENTS We would like to thank Dr. Pavel Petrov (Pacific Oceanological Institute, Vladivostok, Russia) for sharing his user-friendly RAMs (M. Collins/NRL) Matlab code and advices in operation. 5. REFERENCES [1] Lorke, A. (2007). “Boundary mixing in the thermocline of a large lake,” J. Geophys. Res. 112, C09019, doi:10.1029/2006JC004008. [2] Ostrovsky, I., and Sukenik, A. (2008). “Spatial heterogeneity of biogeochemical parameters in a subtropical lake,” in Monitoring and Modeling Lakes and Coastal Environments, edited by P. K. Mohanty (Springer, New York), pp. 79–90 [3] Katsnelson, B., Petnikov, V., & Lynch, J. (2012). Fundamentals of shallow water acoustics. Springer (Vol. 1). http://doi.org/10.1017/CBO9781107415324.004 [4] Kuz’kin, V. M., Lin, Y-T., Lunkov, A. A., Lynch, J. F., and Petnikov, V. G. (2011). “Frequency shifts of the sound field interference pattern on oceanic shelf in summer conditions,” Acoust. Phys. 57(3), 381–390. [5] Pedlosky, J. (2003). Waves in the Ocean and Atmosphere: Introduction to Wave Dynamics (Springer, Berlin), 268 pp. [6] Filatov, A., Terzevik, R., Zdorovennov, V., Vlasenko, V., Stashchuk, N., and Hutter, K. (2012). Field Studies of Non-Linear Internal Waves in Lakes on the Globe, edited by K. Hutter (Springer, New York), pp. 23–103. [7] Lemckert C. J., and Imberger, J. (1998). “Turbulent benthic boundary layer mixing events in fresh water lakes,” in Physical Processes in Lakes and Oceans, Coastal and Estuarine Studies Series, edited by J. Imberger (AGU, Washington, DC), Vol. 54. [8] Katsnelson, B., Lunkov, A., & Ostrovsky, I. (2016). Interference pattern of the sound field in the presence of an internal Kelvin wave in a stratified lake. The Journal of the Acoustical Society of America, 139(2), 881–889. http://doi.org/10.1121/1.4941658 [9] Katsnelson, B., Katsman, R., Lunkov, A., & Ostrovsky, I. (2017). Acoustical methodology for determination of gas content in aquatic sediments, with application to Lake Kinneret, Israel, as a case study. Limnology and Oceanography: Methods, 15(6), 531–541. https://doi.org/10.1002/lom3.10178
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ASSESSMENT OF RELIABILITY OF MULTI-BEAM ECHO-SOUNDER BATHYMETRIC UNCERTAINTY PREDICTION MODELS Tannaz H. Mohammadlooa, Mirjam Snellen a, Alireza Amiri-Simkooeia, Dick G. Simonsa a
Acoustics Group, ANCE, Department of Control & Operations, Faculty of Aerospace Engineering, Delft University of Technology, Kluyverweg 1, 2629 HS, Delft, The Netherlands Tannaz H. Mohammadloo, Kluyverweg 1, 2629 HS, Delft, The Netherlands, 0031 15 2787399, T.Hajimohammadloo@tudelft.nl Abstract: Nowadays Multi-Beam Echo-Sounder (MBES) systems are used for obtaining information of the sea/river bed bathymetry and sediment composition. For the latter, use is usually made of the backscatter strength and depth derivatives, such as depth residuals. However, the depth derivatives are affected by the uncertainties inherent to the MBES varying with the sensors used, survey configuration and operational environment. Although models are available for the vertical uncertainty prediction, the question is how well these models can capture the estimated uncertainties of real observations. The present contribution addresses this issue by comparing the measured with modelled depth uncertainty accounting for the most recent insights of the error contributors. Data was acquired in water depths of around 2m, 10m and 30m with pulse lengths of 27 µs, 54 µs and 134 µs in the Oosterschelde estuary, the Netherlands, enabling the assessment of depth and pulse length dependence of the uncertainties. In general, the predicted and measured uncertainties are in the same order of magnitude. With increasing depth the discrepancy between the modelled and measured uncertainties increases. The effect of changing pulse length is found to be captured by the model, except for the angles close to nadir. The most dominant contributors to the vertical uncertainty are those induced by the angle of impact and range measurements. These contributors thus require further investigation to obtain a more realistic estimate of the vertical uncertainties. Keywords: Multibeam Echosounder Derived Depth, Bathymetric Uncertainty Prediction, Inherent MBES Uncertainty Sources
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1. INTRODUCTION Multibeam echosounders (MBESs) have become the most valuable tool for seafloor mapping providing a good coverage and high resolution bathymetrty and acoustic backscatter datasets within a relatively short time [1]. The applications of these datasets are numerous, including offshore activities such as building wind farms at sea, the support of dredging operations, safe navigation, and the study of marine geological and biological systems [2], [3], [4], [5]. The MBES measurements used for obtaining such information are backscatter strength and depth derivatives, such as depth residuals. However, similar to any type of measured quantity, they are contaminated by uncertainties. Obtaining a realistic a priori estimate of depth uncertainties is thus of importance as lacking information can lead to mistakenly classifying the uncertainties and assigning different sediment types to measurement having actually the same sediment composition, but different uncertainties. The realistic uncertainty description is also used for survey planning to assess whether the required survey standards can be met in a specific measurements campaign. Other applications of a-priori estimates of the vertical uncertainty include, but are not limited to, bathymetric gridding, [6], used for coastal inundation modelling, [7]. Efforts have been thus put forward to predict the depth uncertainties [8], [7], [9] and [10]. However, considering the fast development of MBES systems, there is a need to investigate the reliability of MBES depth uncertainty prediction models using real measurements to obtain an insight into their agreements and possible discrepancies and to highlight direction for future improvements. The present contribution makes the first steps toward such a comparison and it is organized as the following. Section 2 gives a short description of the equations used for quantifying the depth uncertainties followed by the result in Section 3. The conclusions are given in Section 4.
2. DESCRIPTION OF THE DEPTH UNCERTAINTY PREDICTION MODEL In an MBES system the depth of an underlying surface is determined using the travel time of a transmitted signal. A wide bundle of sound is transmitted perpendicular to the direction of vessel movement. Using electronic beam-steering at reception, the MBES can distinguish between incoming angles of sound on the transducer, [11]. The steering angle is noted by đ?œƒđ?œƒđ?‘ đ?‘ and the resulting beams have opening angles đ?œ“đ?œ“đ?‘Śđ?‘Ś and đ?œ“đ?œ“đ?‘Ľđ?‘Ľ in the across-track and along-track directions respectively. The depth below the transducer, d, is determined as đ?‘‘đ?‘‘ = đ?‘&#x;đ?‘&#x; cos đ?‘ƒđ?‘ƒ cos(đ?œƒđ?œƒđ?‘ đ?‘ + đ?‘…đ?‘… + đ?œƒđ?œƒmount ) = đ?‘&#x;đ?‘&#x; cos đ?‘ƒđ?‘ƒ cos đ?œƒđ?œƒ
(1)
with đ?œƒđ?œƒmount , R and P being the across track angle under which the MBES is mounted on the vessel, roll and pitch angle, respectively. Three angles represent rotations around the depthaxis and can be combined in a single rotation đ?œƒđ?œƒ = đ?œƒđ?œƒđ?‘ đ?‘ + đ?‘…đ?‘… + đ?œƒđ?œƒmount . Uncertainty with regard to the exact values of the parameters used for depth calculation induces a depth uncertainty. Here, a short description of the contributions is given (an interested reader might refer to [8] for a complete derivation of all equations). I.
Echo-Sounder contribution which can be divided into range and angular errors. The former is induced by uncertainties in the measured travel time of the signal and speed
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of sound. The across-track angular error is due to the uncertainty in the measurement of the angle of impact of the incoming sound wave at the transducer array. The 2 combined uncertainty, đ?œŽđ?œŽđ??¸đ??¸đ??¸đ??¸â„Žđ?‘†đ?‘†đ?‘†đ?‘†đ?‘†đ?‘† , reads as 2 đ?œŽđ?œŽđ??¸đ??¸đ??¸đ??¸â„Žđ?‘†đ?‘†đ?‘†đ?‘†đ?‘†đ?‘† = (đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?‘ƒđ?‘ƒ đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?œƒđ?œƒ)2 ďż˝đ?œŽđ?œŽđ?‘&#x;đ?‘&#x;2đ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š + ďż˝
đ?‘&#x;đ?‘&#x;đ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š 2 2 ďż˝ đ?œŽđ?œŽđ?‘Łđ?‘Łđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š ďż˝ + (đ?‘&#x;đ?‘&#x; đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?‘ đ?œƒđ?œƒ đ?‘?đ?‘?đ?‘?đ?‘?đ?‘?đ?‘? đ?‘ƒđ?‘ƒ)2 đ?œŽđ?œŽđ?œƒđ?œƒ2đ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š đ?‘Łđ?‘Łđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š
(2)
where đ?œŽđ?œŽđ?‘&#x;đ?‘&#x;2meas is the error in the measured distance (đ?‘&#x;đ?‘&#x;meas ). It depends on the range sampling resolution and pulse length. Equation (2) is valid under the assumption that the true sound speed in the water (v) does not deviate too much from the measured average sound speed in the water column (đ?‘Łđ?‘Łmeas ). đ?œŽđ?œŽđ?œƒđ?œƒ2meas is the random error in the measurements of the impact angle and depends on the measurement method (phase or amplitude detection), as
đ?œŽđ?œŽđ?œƒđ?œƒmeas
II.
đ?œ“đ?œ“
⎧ đ?‘Śđ?‘Ś ⎪ 12 = 0.2đ?œ“đ?œ“ đ?‘Śđ?‘Ś ⎨ ⎪ đ?‘›đ?‘› ⎊ ďż˝ đ?‘?đ?‘?
Amplitude detection Phase detection
(3)
đ?‘‘đ?‘‘ đ?œ“đ?œ“
đ?œ?đ?œ?đ?œ?đ?œ?
with np the number of phase samples, defined as (cos2đ?‘Śđ?‘Śđ?œƒđ?œƒ)ďż˝(2 sin đ?œƒđ?œƒ ) where đ?œ?đ?œ? indicates the pulse length. In Ref. [8], it is indicated that the phase detection is applied for situations in which đ?‘›đ?‘›đ?‘?đ?‘? > 12. For the present contribution, the detection type is chosen automatically based on the minimum of angular measurements errors induced by the amplitude and phase detection. Equation (3) does not take the impact of a particular pulse shape into account. An alternative approach presented in [9] does account for the uncertainties in the detection instant in the interferometry step using the coherence coefficient which is a measure for the correlation between the two received signals at the two sub-arrays. In this case the pulse shape is taken into account. Angular Motion Sensor contribution due to the uncertainty in the measurement of roll and pitch denoted by đ?œŽđ?œŽđ?‘…đ?‘…2đ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š and đ?œŽđ?œŽđ?‘ƒđ?‘ƒ2đ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘šđ?‘š , respectively. The resulting random depth error reads as 2 đ?œŽđ?œŽAng_Mot = (đ?‘&#x;đ?‘&#x; cos đ?‘ƒđ?‘ƒ sin đ?œƒđ?œƒ)2 đ?œŽđ?œŽđ?‘…đ?‘…2meas + (đ?‘&#x;đ?‘&#x; cos đ?œƒđ?œƒ sin đ?‘ƒđ?‘ƒ)2 đ?œŽđ?œŽđ?‘ƒđ?‘ƒ2meas
(4)
In case of applying roll and pitch corrections during beamforming, a second pair of roll and pitch errors are added due to account for the errors made by this stabilization. III.
Motion Sensor/Transducer Alignment contribution due to incorrect alignment of the motion sensor, i.e., roll (∆đ?‘…đ?‘…Align ) and pitch (∆đ?‘ƒđ?‘ƒAlign ), with the MBES transducer and induces a depth uncertainty as 2 2 2 đ?œŽđ?œŽMotSen_Align = (đ?‘&#x;đ?‘&#x; cos đ?‘ƒđ?‘ƒ sin đ?œƒđ?œƒ)2 đ?œŽđ?œŽâˆ†đ?‘…đ?‘… + (đ?‘&#x;đ?‘&#x; cos đ?œƒđ?œƒ sin đ?‘ƒđ?‘ƒ)2 đ?œŽđ?œŽâˆ†đ?‘ƒđ?‘ƒ Align Align
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(5)
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IV.
where the first and second terms in Eq. (5) indicate the contribution of roll and pitch incorrect alignment, respectively. Again, if mechanical pitch and roll stabilization is 2 2 active, a second pair of đ?œŽđ?œŽâˆ†đ?‘…đ?‘… and đ?œŽđ?œŽâˆ†đ?‘ƒđ?‘ƒ is added to Eq. (5). align align 2 Sound Speed contribution due to errors in the sound speed at the transducer, đ?œŽđ?œŽđ?‘Łđ?‘Ł,đ?‘ đ?‘ , 2 and that of the water column, đ?œŽđ?œŽđ?‘Łđ?‘Ł,đ?‘?đ?‘? . These uncertainties induce uncertainty in the steering angle and consequently the measured depth. The contribution reads as 2 đ?œŽđ?œŽSoundSpeed
V.
2
tan đ?œƒđ?œƒđ?‘ đ?‘ 2 2 tan đ?œƒđ?œƒ 2 = (đ?‘&#x;đ?‘&#x; cos đ?‘ƒđ?‘ƒ sin đ?œƒđ?œƒ) �� ďż˝ đ?œŽđ?œŽđ?‘Łđ?‘Ł,đ?‘ đ?‘ + ďż˝ ďż˝ đ?œŽđ?œŽđ?‘Łđ?‘Ł,đ?‘?đ?‘? ďż˝ đ?‘Łđ?‘Łđ?‘ đ?‘ 2đ?‘Łđ?‘Łđ?‘?đ?‘? 2
(6)
where the first term in brackets in Eq. (6) represents errors in the beam steering angle due to fluctuations of the sound speed at the transducer and depends on the angle relative to the normal of the transducer, đ?œƒđ?œƒđ?‘ đ?‘ , and đ?‘Łđ?‘Łđ?‘ đ?‘ is the surface sound speed. The second term in bracket in Eq. (6) is the contribution of the non-uniform sound speed profile to the random depth error. Varying sound speed in the water column result in the deviation of the sound waves from straight lines. This is accounted for in the MBES processing but uncertainties in the water column sound speed profile measurements induce errors in the resulting bathymetry. The above expression for the quantification of this errors source is based on the assumption a two-layer sound speed profile. Heave contribution due to measurements error of heave and roll and pitch errors. The depth uncertainty due to the heave contribution reads as đ?œŽđ?œŽđ??ťđ??ť2 = max(đ?‘Žđ?‘Ž2 , (đ?‘?đ?‘?đ??ťđ??ťmeas )2 ) + đ?œŽđ?œŽđ??ťđ??ť2Induced
(7)
where the first term in Eq. (7) represents the uncertainty in the heave measurements with a being a static component in meters and b a variable component. In case of using the Global Navigation Satellite System (GNSS) sensor for heave measurements, the first term is substituted with the uncertainty of the sensor’s z-component. Heave induced uncertainty is due to the errors in the roll and pitch measurements, their alignment and the fact that the vertical reference unit, VRU, (or GNSS sensor in case of using it for heave determination) is not located at the same position as the transducer resulting in a deviation between the measured heave and the heave at the transducer. The corresponding induced depth uncertainty reads as 2 đ?œŽđ?œŽđ??ťđ??ť2Induced = (đ?‘Ľđ?‘Ľ cos đ?‘ƒđ?‘ƒ − đ?‘Śđ?‘Ś sin đ?‘…đ?‘… sin đ?‘ƒđ?‘ƒ − đ?‘§đ?‘§ cos đ?‘…đ?‘… sin đ?‘ƒđ?‘ƒ)2 ďż˝đ?œŽđ?œŽđ?‘ƒđ?‘ƒ2meas + đ?œŽđ?œŽâˆ†đ?‘ƒđ?‘ƒ ďż˝ Align 2 + (đ?‘Śđ?‘Ś cos đ?‘…đ?‘… cos đ?‘ƒđ?‘ƒ − đ?‘§đ?‘§ sin đ?‘…đ?‘… cos đ?‘ƒđ?‘ƒ)2 ďż˝đ?œŽđ?œŽđ?‘…đ?‘…2meas + đ?œŽđ?œŽâˆ†đ?‘…đ?‘… ďż˝ Align + sin2 đ?‘ƒđ?‘ƒđ?œŽđ?œŽđ?‘Ľđ?‘Ľ2 + sin2 đ?‘…đ?‘…cos2 đ?‘ƒđ?‘ƒđ?œŽđ?œŽđ?‘Śđ?‘Ś2 + (1 − cos đ?‘…đ?‘… cos đ?‘ƒđ?‘ƒ)2 đ?œŽđ?œŽđ?‘§đ?‘§2
(8)
where x, y, and z are the offset coordinates between the transducer and VRU. Here, đ?œŽđ?œŽđ?‘Ľđ?‘Ľ2 , đ?œŽđ?œŽđ?‘Śđ?‘Ś2 and đ?œŽđ?œŽđ?‘§đ?‘§2 are the errors in the measurement of the distance between the VRU and MBES. In case the vertical positioning is carried out using the GNSS and the position corrections are received from Real Time Kinematic (RTK) services, the depth relative to the vertical datum is derived and accounting for height offset, such as dynamic
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draft and tidal variations, in not of importance. Otherwise, a separate contribution will be added, see [8]. Assuming the above contributors are uncorrelated, the total depth error is calculated as
2 2 2 2 đ?œŽđ?œŽđ?‘‘đ?‘‘2 = ďż˝đ?œŽđ?œŽđ??¸đ??¸đ??¸đ??¸â„Žđ?‘†đ?‘†đ?‘†đ?‘†đ?‘†đ?‘† + đ?œŽđ?œŽAng_Mot + đ?œŽđ?œŽMotSen_Align + đ?œŽđ?œŽSoundSpeed + đ?œŽđ?œŽđ??ťđ??ť2
(9)
3. RESULTS This section focuses on quantifying the predicted depth uncertainties for different environmental conditions and operation setting. The predictions are derived for a situation where the EM2040c MBES (manufactured by Kongsberg) is used, and its characteristic are thus used as the input parameters, see [12]. In addition to the MBES characteristics, information regarding the uncertainties of the sound speed measurements and motion sensors are of importance. For this study a case is considered where the inertial navigation sensor and sound velocity profiler used are Phins (manufactured by iXblue) with nominal roll and pitch accuracies equalling 0.01° and miniSVP (manufactured by Valeport) with a nominal accuracy of 0.02m/s, respectively, see [13] and [14]. However, from measurements in different locations (inland waterways and the North Sea), the uncertainty of the latter was found to be 0.2m/s, and hence this value is chosen as a more realistic description of the system’s accuracy. The sound velocity profile acquired was almost constant through the water column equalling 1515 m/s. Shown in Fig. 1 is the total predicted vertical uncertainty as a function of depth for pulse lengths of 27 ¾s, (a), and 134 ¾s, (b) for 4 different beam angles. As seen, an increase in the pulse length deteriorates the vertical accuracy for all beam angles. For beams close to nadir, the increased uncertainty with depth (black solid line) is negligible, however, as the beam angle increases, the vertical uncertainty increases to a larger extent.
Fig. 1: Predicted depth uncertainty as a function of depth for pulse length of 27 ¾s (a) and 134 ¾s (b) Shown in Fig. 2 is the total vertical uncertainty predicted as a function of pulse length for depths of 2 m, (a), and 30 m, (b). For the depth of 2 m and shortest pulse length, the predicted uncertainty is almost equal for all the beam angles. As the pulse length increases, the discrepancy between the predictions for varying beam angle increases with the predictions being larger at nadir. As one moves to deeper depths, (b), the behavior of the predictions changes compared to those of the shallower depth. This means that for the shortest pulse length, the uncertainty increases toward the outer parts of the swath. As seen, for the beam
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away from nadir (20° (green), 40° (red) and 60° (blue)) the predictions show almost equal increase in the uncertainty with the pulse length. However, this is not the case for shallower depth as the rate of the increase with the pulse length decreases towards the outer parts of the swath.
Fig. 2: Predicted depth uncertainty as a function of pulse length for water depth of 2 m (a) and 30 m (b) The assessment of the dependency of the predicted uncertainties to depth, beam angle and pulse length indicates that varying MBES settings and operational environments can significantly affect the vertical uncertainties. Therefore, a realistic description of this parameter is needed for employing the depth derivatives, such as depth residuals ( [4] and [15]), as a potential classifier in seafloor sediment classification methods. This means that for a certain MBES setting and operational environment, lacking knowledge of the vertical uncertainties can lead to interpreting the observed depth variations as varying sediment types although they might be induced by the depth uncertainty sources inherent to the MBES. To assess the reliability of the uncertainty prediction model, the predicted depth uncertainty is compared to that measured. Shown in Fig. 3 is the measured (blue) and total (cyan) predicted vertical standard deviation in water depth of 10 m with pulse length of 27 µs (a) and 134 µs (b). The contribution of the individual sources is also shown. The black vertical lines indicate the beam angle where the switch from the amplitude to phase detection occurs based on the minimum standard deviation induced by both detection methods. The blue squares and asterisks represent the amplitude and phase detection based on the measurements, respectively. The theoretical beam angle corresponding to the change in the bottom detection method does not coincide with the one found for the measurements. This indicates that the criteria used for obtaining this transition point require modification possibly by accounting for the characteristics of the received signal. As the pulse length increases, a broader range of beam angles around nadir (both based on predictions and measurements) use amplitude detection which is due to the phenomenon referred to as the baseline decorrelation. Longer pulse length means larger footprints on the seafloor, and hence a more fluctuating directivity pattern resulting in a noisy estimate of the zero-crossing of the phase difference (used for the phase detection). Therefore, amplitude detection becomes the preferred bottom detection approach. Generally, for both pulse lengths, the predicted and measured uncertainties are in the same order of magnitude with larger uncertainties for longer pulse lengths (b). For the shorter pulse length, there is a good agreement for the beams larger than 10° and the discrepancies occur for the beams closer to nadir. Potential improvements in the agreement can be obtained by further investigation of the term associated to the echosounder contribution (see the black circles) as 1) it is the most
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dominant uncertainty source and 2) it depends on the bottom detection approach. As for the measurements with the longer pulse length, slightly better agreement between the measured and modelled uncertainties is obtained for starboard. The larger variations in depth uncertainty for the port side can be due to the existence of a strong bottom morphology. Here, the results are derived by subtracting the mean depth per small surfaces. However, for some areas, this will not fully account for the changes in the bathymetry induced due to the presence of local slopes. One approach to address this issue is to fit a bi-quadratic or linear function to the measurements located within small surfaces and calculate the standard deviation which is now under further investigation.
Fig. 3. Measured vertical standard deviation and those predicted for water depth of 10 m and pulse lengths of 27 µs (a) and 134 µs (b). The black vertical lines shows the location the beam angle where the switch from amplitude to the phase detection occurs according to the criterion of minimum standard deviation. 4. CONCLUSIONS The performance of the sediment classification approaches can be potentially improved by including the depth derivatives, such as depth residuals. However, these depth measurements are contaminated by the uncertainties in the measured depths depending on a number of parameters, such as survey configuration. A realistic description of the vertical uncertainties is thus required to ensure that the variation of the depth derivatives is due to the variation in the sediment type and not induced by the vertical uncertainties. The modelled and measured vertical standard deviation are thus compared to assess their agreement and directions for possible improvements. In general, the predicted and measured uncertainties are in the same order of magnitude. Vertical uncertainty increases with both an increasing pulse length and depth. To obtain a better agreement between the predicted and measured uncertainties, one can 1) account for the presence of local slopes as the model is developed assuming a flat
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seafloor, and 2) further investigate the contribution of the echosounder as it is the most dominant error source with dependency on the detection method. REFERENCES [1] V. Lecours, M. F. J. Dolan, A. Micallef and V. L. Lucieer, "A review of marine geomorphometry, the quantitative study of the seafloor," Hydrology amd Earth System Science, vol. 20, p. 3207–3244, 2016. [2] J. J. Daniell, P. T. Harris, M. G. Hughes, M. Hemer and A. Heap, "The potential impact of bedform migration on seagrass communities," Continental Shelf Research, vol. 28, no. 16, p. 2188–2202, 2008. [3] V. E. Kostylev, B. J. Todd, G. B. J. Fader, R. C. Courtney, G. D. M. Cameron and R. A. Pickrill, "Benthic habitat mapping on the Scotian Shelf based on multibeam bathymetry, surficial geology and sea floor photographs," Marine Ecology Progress Series, vol. 219, pp. 121-137, 2001. [4] D. Eleftherakis, A. R. Amiri-Simkooei, M. Snellen and D. G. Simons, "Improving riverbed sediment classification using backscatter and depth residual features of multibeam echo-sounder systems," J. Acoust. Soc. Am. , vol. 131, no. 5, pp. 3710-3725, 2012. [5] J. E. Hughes Clarke, L. A. Mayer and D. E. Wells, "Shallow-water imaging multibeam sonars: A new tool for investigating seafloor processes in the coastal zone and on the continental shelf," Marine Geophysical Researches, vol. 18, no. 6, p. 607–629, 1996. [6] B. R. Calder and L. A. Mayer, "Automatic processing of high-rate, high-density multibeam echosounder data," Geochem Geophys Geosyst, vol. 4, no. 6, pp. 1048-1069, 2003. [7] R. Hare, B. Eakins, C. Amante and L. A. Taylor, "Modeling bathymetric uncertainty," in US Hydro 2011, Tampa, FL, 2011. [8] R. Hare, "Error Budget Analysis for US Naval Oceanographic Office (NAVOCEANO) Hydrographic Survey Systems," Hydrographic Science Research Center (HSRC), Mississippi, 2001. [9] T. H. Mohammadloo, M. Snellen and D. G. Simons, "Multi-beam echo-sounder bathymetric measurements: Implications of using frequency modulated pulses," The Journal of the Acoustical Society of America, vol. 144, no. 2, pp. 842-860, 2018. [10] X. Lurton and J. M. Augustin, "A measurement quality factor for swath bathymetry sounders," IEEE J. Ocean. Eng., vol. 35, no. 4, pp. 852-862, 2010. [11] X. Lurton, An Introduction to Underwater Acoustics, Principles and Applications, Second edition, Springer-Verlag Berlin Heidelberg, 2010, pp. 167-224, 225-270. [12] Kongsberg, EM 2040C, Kongsberg, 2017. [13] Phins: FOG-based high-performance inertial navigation system, iXblue, March 2017. [14] miniSVP - Sound Velocity Profiler, Valeport, May 2016. [15] D. Eleftherakis, M. Snellen, A. Amiri-Simkooei and D. G. Simons, "Observations regarding coarse sediment classification based on multi-beam echo-sounder’s backscatter strength and depth residuals in Dutch rivers," Journal of Acoustical Society of America, vol. 135, p. 3305, 2014.
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DEVELOPMENT OF ACOUSTIC DOPPLER FISH MONITORING FOR APPLICATIONS IN HIGH-ENERGY TIDAL CHANNELS
Muriel Dunna, Len Zedela, Greg Trowseb a
Department of Physics and Physical Oceanography, Memorial University of Newfoundland, St. John’s, NL b Luna Ocean Consulting Ltd., Westport, NS. Muriel Dunn, Chemistry-Physics Building, 283 Prince Philip Drive, St. John’s, NL, A1B 3X7, Canada. mbdunn@mun.ca
Abstract: Acoustic Doppler profiler systems (ADCPs) are the standard instrument used to monitor ocean currents. These instruments also detect signals scattered by fish, but these signals are normally treated as noise and rejected by ADCP data processing techniques. Those rejected signals do however contain information on fish movement providing an opportunity to extend the application of ADCP technology. The added capability of the ADCP provides a monitoring tool for fish activity and presence in areas that may be impacted by future in-stream hydro energy development projects. We explore this capability through a 37-day deployment of a self-contained bottom-mounted frame equipped with a 600 kHz RD Instruments Workhorse ADCP alongside a 120 kHz BioSonics DTX Submersible Split Beam Echo Sounder system. The deployment took place in Grand Passage, Nova Scotia, at a depth of 25m, where the tidal range is ~5m, and the currents are up to 2.5m/s. We chose this site because it is located in a tidal channel that is identified as having the potential for in-stream tidal generation. Anticipating possible interference, we selected different frequencies and regulated the duty cycles to overlap for half of the total sampling time to assure the collection of uncontaminated data. The splitbeam echo sounder shows plumes of scatterers, presumably bubbles, emanating from the surface. Discrete targets are detected throughout the water column within fish schools and as individuals. The corresponding ADCP data detects the same fish schools by using coinciding instances of high intensity and high correlation. The ADCP signal has a lower range resolution but shows less contamination from surface bubbles and weaker scatterers. We present observations for both instruments and contrast their capabilities. Keywords: Renewable energy, acoustic Doppler current profiler, fish detectability
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1. INTRODUCTION Signals received by ADCPs contain backscatter from a variety of objects in the water column, including fish, zooplankton, sediments, bubbles and the surface or bottom. As the instrument is used for water velocity measurements, typically all signals that differ significantly in echo intensity between the four beams are rejected to reduce fish bias in velocity data [1]. Efforts to use Doppler sonar for fish velocity measurements before the 2000s used low frequency, long pulses and narrow bandwidths [2]. Though Doppler shift methods were said to, theoretically, be promising in gathering information for fish behaviour, the techniques and instruments had not been developed adequately for this purpose. Demer (2000) suggested that ADCPs need to achieve higher velocity and range resolution and showed that a higher frequency and autocorrelation for the detection of pulse shifts wouldaccomplish that. Demer also identified a need to modify parameters and data processing as well as carefully selecting survey sites with fish schools matched to the size of the processing bins [3]. Zedel and Cyr-Racine (2009) presented an alternative approach to analyzing Doppler sonar data using a least-squares based algorithm which analyzes each acoustic beam individually to extract both fish and water velocities, even when fish are intermittently present [4]. Even with a suitable processing algorithm, the challenge remains to identify the presence of fish in ADCP data accurately. The need for such an evaluation motivates this study which uses the opportunity presented by the need to monitor fish presence and behaviour in regions of in-stream tidal energy generation. Such areas are inherently difficult for sampling when using conventional boat mounted acoustic surveys. This paper reports on a month-long deployment of a collocated bottom-mounted ADCP and split-beam echosounder to evaluate fish detectability in ADCP data.
Fig.1: Experimental site. Left: location of the deployment in Nova Scotia. Right: deployment location in Grand Passage (STAR), (extent of the ferry route is the chequered red area).
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2. METHODOLOGY
2.1 Experimental Site & Deployment Grand Passage is a tidal channel in Nova Scotia, Canada that is identified as having the potential for in-stream tidal developments. Prior studies of fish presence in high-energy tidal channels have identified common survey challenges to be acoustic scattering from near-surface bubbles. The study by Melvin & Cochrane (2014) recommends deploying “an autonomous, stationary, bottom-mounted echo-sounder” to overcome these challenges [5]. This experimental site further motivates that approach because it has frequent boat and ferry traffic. A long-term bottom-deployment also allows for inter-survey data which can validate the “snapshot” measurements from conventional surveys and separate the diurnal and tidally induced fish behaviour. The bottom-mounted self-contained frame was deployed at 25m depth and positioned, as shown in Figure 1; note the location selected to avoid interference from the ferry traffic. The frame was mounted with an RD Instruments 600kHz acoustic Doppler current profiler and a BioSonicsDTX Submersible system with 120kHz split-beam echosounder, as shown in Figure 2. The system was deployed for 37 days, from September 21st, 2018 14:30 to October 29th, 2018 13:54. The acoustic instruments were installed as close as possible to facilitate direct comparison between both datasets.
Fig.2: Left: A picture of the frame before the deployment. Right: Sketch of the frame.
2.2 Instrument configuration and calibration A common problem when operating two nearby acoustic instruments is interference between the two instruments. We chose widely separated frequencies to mitigate this problem (120kHz for split-beam and 600kHz for the ADCP). Nevertheless, we observed contamination in the split-beam sonar data during lab tests. Therefore, in addition to the separate frequencies, we staggered the duty cycle to ensure the collection of both individual uncontaminated data as well as simultaneous measurements. The ADCP was set to collect at one ping per second with 1m bins with no averaging of the profile data; the sampling duty cycle was 20 minutes on and 20 minutes off. The split-beam sonar was configured to transmit four pings per seconds at a 0.1ms - 649 -
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pulse duration. The staggered duty cycle was set up by an initial 10 minutes lag relative to the ADCP and a 20 minutes on and 40 minutes off duty cycle. The ADCP compass was calibrated the day before the deployment using the conventional rotation of the mounted frame [6]. The ADCP backscatter calibration coefficients were taken from a January 2018 laboratory calibration. The split-beam was calibrated with a 33.2mm diameter tungsten carbide sphere in the days immediately following the retrieval of the frame; the calculated offset of 1.1dB was applied to the dataset. 2.3 Data Processing 2.3.1
Acoustic Doppler current profiler
Backscatter data were corrected to volume backscatter coefficients using the procedure outlined by Deines (1999) [7]. The presence of fish in the Doppler sonar data can be indicated by volume backscatter levels exceeding a specified threshold, but properties unique to the broadband Doppler system allow discrimination of discrete targets as opposed to volume backscatter. Broadband Doppler systems transmit pulse pairs with a known lag; the phase of the received echoes is used to extract velocity. The magnitude of the autocorrelation of the received pulses is a measure of the quality of the signal. When coincident with high volume backscatter signal, a strong correlation suggests an acoustically discrete target, distinct from the median value correlation characteristic of a cloud of bubbles or a more extensive school of fish [8] The volume backscatter and correlation thresholds, -45dB and 135 counts respectively, are applied to each beam individually for fish detection; an example is shown in Figure 3. To place the correlation threshold into context, normal volume backscatter data would have an average correlation of 128. Conversely; water targets are identified as signals below either the intensity or correlation thresholds. Note in particular the clear agreement in backscatter structure when comparing the Doppler sonar data (Fig 3a) with the split-beam data (Fig 3c).
Fig.3: a) A volume backscatter echogram for a fish school from the ADCP with the identified fish targets (.) from all four beams b) identifies the remaining data after the backscatter threshold (-45dB) is applied, c) the corresponding split-beam signal and d) identifies the remaining data after the backscatter and correlation thresholds (135 counts). - 650 -
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2.3.2
Split-beam echo sounder
Sonar5-Pro [9], a post-processing tool for echosounder data was used to process the splitbeam data. A surface exclusion line is determined by using the bottom detection algorithm but generally requires manual adjustment for areas of combined fish and bubbles signals. The Cross-Filter Detector algorithm was used to identify the SED (single echo detections) and to combine them into fish tracks. This algorithm is composed of two filters: a foreground filter that smooths over the stronger signal with a running mean and a background filter that smooths the weaker signal and adds an offset to minimize the intensity of the weaker signal even further. The combination of these filters isolates the targets into SEDs. These targets are then combined into fish tracks, using the automatic fish tracking algorithm, based on proximity of targets, and length, speed and path of tracks. Only the SEDs included in tracks, and the whole tracks themselves were used to compare with the fish targets identified in the ADCP data.
3.
RESULTS
A total of 37h of simultaneous data were collected between September 21st and October 1st in 10 minutes intervals. The accepted fish detections for both instruments were averaged over 2-hour time bins and 2m depth bins to make a comparison. A particular challenge in strong tidal flows is the occurrence of near-surface bubble plumes [5] which are often hard to distinguish from fish (or plankton) targets. With the Doppler sonar, we used the requirement of high signal correlations to distinguish discrete targets from bubble clouds. The capability is demonstrated through the data present in Figure 3, note the difference between Fig 3b and 3d, where the correlation threshold effectively removes the bubble plumes. For split-beam sonar data, most bubbles were avoided by using the surface exclusion line algorithm. That algorithm is not always correct and requires a user to review areas of spread bubble plumes or regions where fish and bubbles are mixed. In these areas, it becomes a somewhat arbitrary delineation to distinguish desirable data from bubble clouds. This process is irreproducible and time-consuming.
Fig.4: Linear regression analysis for comparison of ADCP fish targets dataset to splitbeam fish track counts and SED within fish tracks count. - 651 -
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The ADCP and the split-beam sonar were evaluated by comparing depth-integrated counts over 2h time intervals. A linear regression model (Figure 4) between the two data sets has a slope of 1.04 ± 0.03 for the fish tracks and 6.7 ± 0.2 for the SED. The SEDs and fish track comparison show that there are on average seven single echo detections per track. The fish tracks are a slightly better representation of fish targets in the ADCP data and more representative of fish counts. Overall, the tracks provide a strong agreement with a crosscorrelation of 0.881. Discrepancies in counts between the instruments would be expected due to differences in sampling volumes, operating frequency, and measured product. 4.
FURTHER WORK
This data demonstrates that ADCP can detect fish in providing agreement with the industry standard, split-beam echosounder, for the conditions sample in Grand Passage. The ADCP also provides an alternative processing approach for dealing with near-surface bubbles that eliminates the need for the more operator intensive process of determining an exclusion line. Further work will focus on extracting fish velocity from the targets in the ADCP data and comparing those with velocities from fish tracks in split-beam data. 5. ACKNOWLEDGEMENTS This research was conducted as part of a Master’s project with Memorial University of Newfoundland, funded by OERA. We would like to thank Mark Downey for his help with the deployment and his instrumentation expertise and Richard Cheel for designing the frame and his invaluable help throughout the deployment and retrieval. REFERENCES [1] Freitag, H., Plimpton, P. and McPhaden, M. Evaluation of an ADCP fish-bias rejection algorithm Proceedings of OCEANS'93. IEEE, 1993. [2] Holliday, D. Two applications of the Doppler effect in the study of fish schools. Rapp. P.v. Reun. Cons. Int. Explor. Mer, 171, 1977, 21. [3] Demer, D., Barange, M. and Boyd, A. Measurements of three-dimensional fish school velocities with an acoustic Doppler current profiler. Fisheries Research 47.2-3, 2000, 201. [4] Zedel, L. and Cyr-Racine, F.. Extracting fish and water velocity from Doppler profiler data. ICES Journal of Marine Science, 66, 2009, 1849. [5] Melvin and Cochrane, Final Report: Investigating the Vertical Distribution, Movement and Abundance of Fish in the Vicinity of Proposed Tidal Power Energy Conversion Devices. 2014 [6] Instruments, R. D. Workhorse Sentinel ADCP user’s guide. 2001. [7] Deines, K. Backscatter estimation using broadband acoustic Doppler current profilers. Proceedings of the IEEE Sixth Working Conference on Current Measurement (Cat. No. 99CH36331). IEEE, 1999. [8] Tollefsen, C., Zedel, L., Evaluation of a Doppler sonar system for fisheries applications, ICES Journal of Marine Science, Volume 60, Issue 3, 2003, 692. [9] Balk, H. and Lindem, Sonar4 and Sonar5-Pro post-processing systems, Operator manual version 604, CageEye AS, 2017.
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