Trans-dimensional inversion of modal dispersion data on the New England Mud Patch

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Dosso, S. E., Eleftherakis, D., & Chapman, N. R. Trans-dimensional inversion of modal dispersion data on the New England Mud Patch. IEEE Journal of Oceanic Engineering, 45(1), (2020): 116-130, doi:10.1109/JOE.2019.2896389.This paper presents single receiver geoacoustic inversion of two independent data sets recorded during the 2017 seabed characterization experiment on the New England Mud Patch. In the experimental area, the water depth is around 70 m, and the seabed is characterized by an upper layer of fine grained sediments with clay (i.e., mud). The first data set considered in this paper is a combustive sound source signal, and the second is a chirp emitted by a J15 source. These two data sets provide differing information on the geoacoustic properties of the seabed, as a result of their differing frequency content, and the dispersion properties of the environment. For both data sets, source/receiver range is about 7 km, and modal time-frequency dispersion curves are estimated using warping. Estimated dispersion curves are then used as input data for a Bayesian trans-dimensional inversion algorithm. Subbottom layering and geoacoustic parameters (sound speed and density) are thus inferred from the data. This paper highlights important properties of the mud, consistent with independent in situ measurements. It also demonstrates how information content differs for two data sets collected on reciprocal tracks, but with different acoustic sources and modal content.10.13039/100000006-Office of Naval Research 10.13039/100007297-Office of Naval Research Globa

Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone

© The Author(s), 2020. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Thode, A., Wright, D., & Chapman, R. Nonlinear time-warping made simple: a step-by-step tutorial on underwater acoustic modal separation with a single hydrophone. The Journal of the Acoustical Society of America, 147(3), (2020): 1897, doi:10.1121/10.0000937.Classical ocean acoustic experiments involve the use of synchronized arrays of sensors. However, the need to cover large areas and/or the use of small robotic platforms has evoked interest in single-hydrophone processing methods for localizing a source or characterizing the propagation environment. One such processing method is “warping,” a non-linear, physics-based signal processing tool dedicated to decomposing multipath features of low-frequency transient signals (frequency f  1 km). Since its introduction to the underwater acoustics community in 2010, warping has been adopted in the ocean acoustics literature, mostly as a pre-processing method for single receiver geoacoustic inversion. Warping also has potential applications in other specialties, including bioacoustics; however, the technique can be daunting to many potential users unfamiliar with its intricacies. Consequently, this tutorial article covers basic warping theory, presents simulation examples, and provides practical experimental strategies. Accompanying supplementary material provides matlab code and simulated and experimental datasets for easy implementation of warping on both impulsive and frequency-modulated signals from both biotic and man-made sources. This combined material should provide interested readers with user-friendly resources for implementing warping methods into their own research.This work was supported by the Office of Naval Research (Task Force Ocean, project N00014-19-1-2627) and by the North Pacific Research Board (project 1810). Original warping developments were supported by the French Delegation Generale de l'Armement

Estimation haute résolution des nombres d'onde dans un guide d'onde océanique dispersif

International audienceLa propagation acoustique en petit fond et aux basses fréquences est décrite par la somme des modes d'un guide d'onde dispersif. Les propriétés physiques du milieu (sédiment, hauteur de la colonne d'eau, etc.) peuvent être inversées à partir des nombres d'onde. Une manière d'estimer ces nombres d'onde consiste à tracer un diagramme f − k (fréquence-nombre d'onde) : c'est le module des transformées de Fourier spatiale et temporelle (TF2D) des signaux reçus sur une antenne horizontale. Cette méthode présente plusieurs limitations liées à l'utilisation de la transformée de Fourier, en particulier, un nombre important de capteurs est requis pour une résolution acceptable des nombres d'onde. L'objectif de ce travail est de réduire le nombre d'hydrophones tout en gardant une bonne séparation des nombres d'onde. Nous proposons d'utiliser un modèle autorégressif des signaux de l'antenne. Le nombre minimal de capteurs nécessaires pour garantir une bonne séparation modale est choisi sur un diagramme de stabilisation. Les diagrammes f −k obtenus par modélisation autorégressive sont tracés pour des données simulées dans un guide de Pekeris et des mesures en mer du Nord. Les diagrammes f−k obtenus par le modèle AR présentent des résolutions modales supérieures tout en utilisant un nombre moindre de capteurs que ceux obtenus par TF2

Méthode d'estimation de l'invariant océanique par couple de modes en acoustique sous-marine passive

International audienceDans de nombreux guides d'onde océaniques, la propagation acoustique est caractérisée par un scalaire que l'on appelle l'invariant océanique. Cette propriété est utilisée dans un grand nombre d'applications pour lesquelles la connaissance de l'invariant océanique est requise. C'est pourquoi des méthodes d'estimation rapide de l'invariant océanique en contexte opérationnel sont nécessaires. L'invariant océanique est classiquement considéré comme un scalaire mais plusieurs études montrent qu'il est mieux modélisé par une distribution en raison de sa dépendance par rapport à la fréquence et aux couples de modes du guide d'onde océanique. Nous présentons une nouvelle méthode d'estimation de l'invariant océanique en milieu petit fond dans une configuration minimale (contexte passif et monocapteur). La méthode vise à obtenir une estimation de l'invariant océanique pour chaque couple de modes, démarche très peu abordée jusqu'à présent. Les performances de l'estimation sont évaluées sur des données simulées. Les résultats obtenus montrent que la méthode permet une estimation précise et robuste au bruit quand le nombre de modes propagatifs n'est pas trop importan

Bi-class classification of humpback whale sound units against complex background noise with Deep Convolution Neural Network

Automatically detecting sound units of humpback whales in complex time-varying background noises is a current challenge for scientists. In this paper, we explore the applicability of Convolution Neural Network (CNN) method for this task. In the evaluation stage, we present 6 bi-class classification experimentations of whale sound detection against different background noise types (e.g., rain, wind). In comparison to classical FFT-based representation like spectrograms, we showed that the use of image-based pretrained CNN features brought higher performance to classify whale sounds and background noise.Comment: arXiv admin note: text overlap with arXiv:1702.02741 by other author

Low-frequency ocean ambient noise on the Chukchi Shelf in the changing Arctic

© The Author(s), 2021. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Bonnel, J., Kinda, G. B., & Zitterbart, D. P. Low-frequency ocean ambient noise on the Chukchi Shelf in the changing Arctic. Journal of the Acoustical Society of America, 149(6), (2021): 4061–4072, https://doi.org/10.1121/10.0005135.This article presents the study of a passive acoustic dataset recorded on the Chukchi Shelf from October 2016 to July 2017 during the Canada Basin Acoustic Propagation Experiment (CANAPE). The study focuses on the low-frequency (250–350 Hz) ambient noise (after individual transient signals are removed) and its environmental drivers. A specificity of the experimental area is the Beaufort Duct, a persistent warm layer intrusion of variable extent created by climate change, which favors long-range acoustic propagation. The Chukchi Shelf ambient noise shows traditional polar features: it is quieter and wind force influence is reduced when the sea is ice-covered. However, the study reveals two other striking features. First, if the experimental area is covered with ice, the ambient noise drops by up to 10 dB/Hz when the Beaufort Duct disappears. Further, a large part of the noise variability is driven by distant cryogenic events, hundreds of kilometers away from the acoustic receivers. This was quantified using correlations between the CANAPE acoustic data and distant ice-drift magnitude data (National Snow and Ice Data Center).This research was supported by the Independent Research and Development Program at WHOI and by the Office of Naval Research (ONR) under Grant Nos. N00014-19-1-2627 and N00014-18-1-2811. J.B. warmly acknowledges D. Cazau (ENSTA Bretagne, France) for helpful discussion and code sharing. The acoustic data collection effort was supported by the ONR under Grant No. N00014-15-1-2196 (Principal Investigator: Y.-T. Lin, WHOI). Thanks also go to crew members of the R/V Sikuliaq and USCGC Healy for assisting in mooring operations. The ITP data were collected and made available by WHOI

TOSSIT: a low-cost, hand deployable, rope-less and acoustically silent mooring for underwater passive acoustic monitoring

© The Author(s), 2022. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Zitterbart, D., Bocconcelli, A., Ochs, M., & Bonnel, J. TOSSIT: a low-cost, hand deployable, rope-less and acoustically silent mooring for underwater passive acoustic monitoring. HardwareX, 11, (2022): e00304, https://doi.org/10.1016/j.ohx.2022.e00304.Passive Acoustic Monitoring (PAM) has been used to study the ocean for decades across several fields to answer biological, geological and meteorological questions such as marine mammal presence, measures of anthropogenic noise in the ocean, and monitoring and prediction of underwater earthquakes and tsunamis. While in previous decades the high cost of acoustic instruments limited its use, miniaturization and microprocessor advances dramatically reduced the cost for passive acoustic monitoring instruments making PAM available for a broad scientific community. Such low-cost devices are often deployed by divers or on mooring lines with a surface buoy, which limit their use to diving depth and coastal regions. Here, we present a low-cost, low self-noise and hand-deployable PAM mooring design, called TOSSIT. It can be used in water as deep as 500 m, and can be deployed and recovered by hand by a single operator (more comfortably with two) in a small boat. The TOSSIT modular mooring system consists of a light and strong non-metallic frame that can fit a variety of sensors including PAM instruments, acoustic releases, additional power packages, environmental parameter sensors. The TOSSIT’s design is rope-less, which removes any risk of entanglement and keeps the self-noise very low.The development of the TOSSIT mooring was supported by a Woods Hole Oceanographic institution Innovative Technology Award (Award number 25226). TOSSIT deployment in Argentina was supported by a Woods Hole Oceanographic Institution Mary Sears visitor award (Award number 24700) and TOSSIT deployments during SBCEX were funded by the Office of Naval Research Task Force Ocean (ONR TFO, Award number: N000141912627). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

STOCHASTIC OPTIMIZATION OVER A PARETO SET ASSOCIATED WITH A STOCHASTIC MULTI-OBJECTIVE OPTIMIZATION PROBLEM

ABSTRACT. We deal with the problem of minimizing the expectation of a real valued random function over the weakly Pareto or Pareto set associated with a Stochastic MultiObjective Optimization Problem (SMOP) whose objectives are expectations of random functions. Assuming that the closed form of these expectations is difficult to obtain, we apply the Sample Average Approximation method (SAA-N, where N is the sample size) in order to approach this problem. We prove that the Hausdorff-Pompeiu distance between the SAA-N weakly Pareto sets and the true weakly Pareto set converges to zero almost surely as N goes to infinity, assuming that all the objectives of our (SMOP) are strictly convex. Then we show that every cluster point of any sequence of SAA-N optimal solutions (N=1,2,. . . ) is almost surely a true optimal solution. To handle also the nonconvex case, we assume that the real objective to be minimized over the Pareto set depends on the expectations of the objectives of the (SMOP), i.e. we optimize over the outcome space of the (SMOP). Then, whithout any convexity hypothesis, we obtain the same type of results for the Pareto sets in the outcome spaces. Thus we show that the sequence of SAA-N optimal values (N=1,2 ...) converges almost surely to the true optimal value. Keywords: Optimization over a Pareto Set, Optimization over the Pareto Outcome Set, Multiobjective Stochastic Optimization, Multiobjective Convex Optimization, Sample Average Approximation Method AMS: 90C29, 90C25, 90C15, 90C26

Désentrelacement de clics par analyse du rythme

National audienceTous les odontocètes émettent de courtes impulsions acoustiques (clics) pour satisfaire une activité de localisation, de chasse ou de communication. Ces impulsions sont le plus souvent émises en trains rythmés. Il est très fréquent d'enregistrer simultanément les clics de plusieurs animaux. Cet article présente une méthode permettant de séparer les trains de clics enregistrés sur un unique hydrophone afin de connaître le nombre d'animaux ayant émis simultanément. La méthode proposée est testée avec succès sur des données simulées et sur des données réelles