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

Inversion of shallow seabed structure and geoacoustic parameters with waveguide characteristic impedance based on Bayesian approach

Underwater acoustic technology is essential for ocean observation, exploration and exploitation, and its development is based on an accurate predication of underwater acoustic wave propagation. In shallow sea environments, the geoacoustic parameters, such as the seabed structure, the sound speeds, the densities, and the sound speed attenuations in seabed layers, would significantly affect the acoustic wave propagation characteristics. To obtain more accurate inversion results for these parameters, this study presents an inversion method using the waveguide characteristic impedance based on the Bayesian approach. In the inversion, the vertical waveguide characteristic impedance, which is the ratio of the pressure over the vertical particle velocity, is set as the matching object. The nonlinear Bayesian theory is used to invert the above geoacoustic parameters and analysis the uncertainty of the inversion results. The numerical studies and the sea experiment processing haven shown the validity of this inversion method. The numerical studies also proved that the vertical waveguide characteristic impedance is more sensitive to the geoacoustic parameters than that of single acoustic pressure or single vertical particle velocity, and the error of simulation inversion is within 3%. The sea experiment processing showed that the seabed layered structure and geoacoustic parameters can be accurately determined by this method. The root mean square between the vertical waveguide characteristic impedance and the measured impedance is 0.38dB, and the inversion results accurately represent the seabed characteristics in the experimental sea area

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

Characterization of sediment dynamics in an estuary environment using acoustic techniques

In recent years, acoustic-based methods have been developed to characterize the dynamical behavior of loose sediments and bed deposits in very shallow water environments. In this paper, we present preliminary results on the estimation of the dynamic changes in an estuarine environment using data from dual-frequency echosounding at high resolution and contemporaneous hydrological measurements including suspended matter concentration, density subbottom profiling, and data assimilation based on a sediment transport model. Those measurements are being conducted in the lower estuary of the Scheldt (Belgium) at the Sint Anna site where strong tide and season-dependent phenomena can be observed. This allows us to construct a ground-truthed, time-dependent geoacoustic model of the environment, i.e., a characterization of sound speed, density, and attenuation in function of time and depth. Synthetic acoustic data generated by that model will then be used to test inversion methods for monitoring sediment dynamics in real time

GEOACOUSTIC INVERSION TECHNIQUES UTILIZING ACOUSTIC VECTOR SENSORS AND RESULTS FROM THE MONTEREY BAY SHELF

The propagation of acoustic waves in shallow water is affected by the seabed properties. Estimating these properties in situ using acoustics is an area of research that has been in development for decades, and many techniques have been proposed using pressure-only sensors. In recent years, vector sensors have been adopted to expand the capabilities of geoacoustic inversion. This dissertation builds upon the findings of Guarino et al. reported in the Journal of Theoretical and Computational Acoustics and in the Proceedings of the 24th International Congress on Acoustics, 24–28 October 2022. It is shown that the combination of pressure and vertical velocity channels of a vector sensor can improve both the estimation of bottom attenuation coefficient, using the modal phase difference approach, and geoacoustic parameters like sound speed and density, using the multichannel average of dispersion curves. In addition, Time-warping, which is a broadly used technique for modes separation, is improved with the inclusion of a band-pass filter masking approach in the time-frequency analysis. Finally, this work suggests that waveform matching should be used as a preliminary step in dispersion curve analysis to improve inversion performance, or even be the primary choice when a vector sensor is available. The results use data collected in Monterey Bay in 2019.Capitao-de-Fragata, Brazilian NavyApproved for public release. Distribution is unlimited

On the use of acoustic particle velocity fields in adjoint-based inversion

Following the recent interest in the use of combined pressure and particle motion sensors in underwater acoustics and signal processing, some general aspects regarding the modeling and multipath phenomenology of acoustic particle velocity fields in shallow water environments have been studied. In this paper we will address a number of issues associated with the incorporation of vector sensor data (pressure and particle velocity) into adjoint-based inversion schemes. Specifically, we will discuss the ability of a semi-automatic adjoint approach to compute the necessary gradient information without the need for an analytic model of the adjoint particle velocity field. Solutions to the forward propagation of acoustic pressure are computed using an implicit finite-difference parabolic equation solver while the particle velocity is calculated locally at each grid point. Some numerical examples of vector sensor inversion results are provided

Tracking of time-evolving sound speed profiles in shallow water using an ensemble Kalman-particle filter

Author Posting. © Acoustical Society of America, 2013. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 133 (2013): 1377-1386, doi:10.1121/1.4790354.This paper presents a tracking technique for performing sequential geoacoustic inversion monitoring range-independent environmental parameters in shallow water. The inverse problem is formulated in a state-space model with a state equation for the time-evolving sound speed profile (SSP) and a measurement equation that incorporates acoustic measurements via a hydrophone array. The particle filter (PF) is an ideal algorithm to perform tracking of environmental parameters for nonlinear systems with non-Gaussian probability densities. However, it has the problem of the mismatch between the proposal distribution and the a posterior probability distribution (PPD). The ensemble Kalman filter (EnKF) can obtain the PPD based on the Bayes theorem. A tracking algorithm improves the performance of the PF by employing the PPD of the EnKF as the proposal distribution of the PF. Tracking capabilities of this filter, the EnKF and the PF are compared with synthetic acoustic pressure data and experimental SSP data. Simulation results show the proposed method enables the continuous tracking of the range-independent SSP and outperforms the PF and the EnKF. Moreover, the complexity analysis is performed, and the computational complexity of the proposed method is greatly increased because of the combination of the PF and the EnKF.This work was supported by the National High Technology Research and Development Program of China (Grant No. 2012AA090901), the National Natural Science Foundation of China (Grant No. 61171147), and the State Key Laboratory of Acoustics, Chinese Academy of Sciences (Grant No. SKLOA201102)

Physics-based characterization of soft marine sediments using vector sensors

In a 2007 experiment conducted in the northern North Sea, observations of a low-frequency seismo-acoustic wave field with a linear horizontal array of vector sensors located on the seafloor revealed a strong, narrow peak around 38 Hz in the power spectra and a presence of multi-mode horizontally and vertically polarized interface waves with phase speeds between 45 and 350 m/s. Dispersion curves of the interface waves exhibit piece-wise linear dependences between the logarithm of phase speed and logarithm of frequency with distinct slopes at large and small phase speeds, which suggests a seabed with a power-law shear speed dependence in two distinct sediment layers. The power spectrum peak is interpreted as a manifestation of a seismo-acoustic resonance. A simple geoacoustic model with a few free parameters is derived that quantitatively reproduces the key features of the observations. This article's approach to the inverse problem is guided by a theoretical analysis of interface wave dispersion and resonance reflection of compressional waves in soft marine sediments containing two or more layers of different composition. Combining data from various channels of the vector sensors is critical for separating waves of different polarizations and helps to identify various arrivals, check consistency of inversions, and evaluate sediment density

An experimental benchmark for geoacoustic inversion methods

© 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., Pecknold, S. P., Hines, P. C., & Chapman, N. R. An experimental benchmark for geoacoustic inversion methods. IEEE Journal of Oceanic Engineering, 46(1), (2021): 261-282, https://doi.org/10.1109/JOE.2019.2960879.Over the past 25 years, there has been significant research activity in development and application of methods for inverting acoustical field data to estimate parameters of geoacoustic models of the ocean bottom. Although the performance of various geoacoustic inversion methods has been benchmarked on simulated data, their performance with experimental data remains an open question. This article constitutes the first attempt of an experimental benchmark of geoacoustic inversion methods. To do so, the article focuses on data from experiments carried out at a common site during the Shallow Water 2006 (SW06) experiment. The contribution of the article is twofold. First, the article provides an overview of experimental inversion methods and results obtained with SW06 data. Second, the article proposes and uses quantitative metrics to assess the experimental performance of inversion methods. From a sonar performance point of view, the benchmark shows that no particular geoacoustic inversion method is definitely better than any other of the ones that were tested. All the inversion methods generated adequate sound-speed profiles, but only a few methods estimated attenuation and density. Also, acoustical field prediction performance drastically reduces with range for all geoacoustic models, and this performance loss dominates over intermodel variability. Overall, the benchmark covers the two main objectives of geoacoustic inversion: obtaining geophysical information about the seabed, and/or predicting acoustic propagation in a given area.Funding Agency: U.S. Office of Naval Research; Ocean Acoustics