Passive geoacoustic inversion in the Mid-Atlantic Bight in the presence of strong water column variability

Tan, Tsu Wei; Godin, Oleg A.; Katsnelson, Boris; Yarina, Marina

doi:10.1121/10.0001333

Cited by 11 publications

(6 citation statements)

References 28 publications

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“…1/2 has a derivative χ (t) relative to t [14,20,28,29]. t r = r/c w is the signal time origin, with c w the sound speed in the waveguide.…”

Section: Estimation Of the Dcs Using Time-warpingmentioning

confidence: 99%

“…To minimize uncertainties in the estimation of seabed parameters, researchers have developed signal processing techniques to derive pertinent details from acoustic field data, such as sound pressure or amplitude phase [9], waveguide invariants [10], reflection coefficient versus angle [11,12] data, etc. The inversion methods using modal amplitude ratio [13], green function [14,15], modal group speed dispersion [16] and vertical coherence [17] have been discussed by Zhou et al [18], while the most widely used characteristic of propagating normal modes is modal group speed dispersion [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

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A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric

Ding,

Zhao,

Yang

et al. 2023

Remote Sensing

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The inversion of acoustic field data to estimate geoacoustic parameters has been a prominent research focus in the field of underwater acoustics for several decades. Modal-dispersion curves have been used to inverse seabed sound speed and density profiles, but such techniques do not account for attenuation inversion. In this study, a new approach where modal-dispersion and waveform envelope data are simultaneously inversed under a multi-objective framework is proposed. The inversion is performed using the Multi-Objective Bayesian Optimization (MOBO) method. The posterior probability densities (PPD) of the estimation results are obtained by resampling from the exploited state space using the Gibbs Sampler. In this study, the implemented MOBO approach is compared with individual inversions both from modal-dispersion curves and the waveform data. In addition, the effective use of the Wasserstein metric from optimal transport theory is explored. Then the MOBO performance is tested against two different cost functions based on the L2 norm and the Wasserstein metric, respectively. Numerical experiments are employed to evaluate the effect of different cost functions on inversion performance. It is found that the MOBO approach may have more profound advantages when applied to Wasserstein metrics. Results obtained from our study reveal that the MOBO approach exhibits reduced uncertainty in the inverse results when compared to individual inversion methods, such as modal-dispersion inversion or waveform inversion. However, it is important to note that this enhanced uncertainty reduction comes at the cost of sacrificing accuracy in certain parameters other than the sediment sound speed and attenuation.

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“…1/2 has a derivative χ (t) relative to t [14,20,28,29]. t r = r/c w is the signal time origin, with c w the sound speed in the waveguide.…”

Section: Estimation Of the Dcs Using Time-warpingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric

Ding,

Zhao,

Yang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Ambient noise interferometry has been used for several applications in acoustic oceanography. In shallow water environments, acoustic mode shapes (Brown et al., 2016; Zang et al., 2015), bottom properties (T. Tan et al., 2018; T. W. Tan et al., 2019, 2020), sound speed (F. Li et al., 2019), and flow velocity (Godin et al., 2014) have been estimated. In the deep ocean, long‐range acoustic propagation in the sound channel has been used to estimated changes in water temperature along the sound channel (K. F. Woolfe et al., 2015).…”

Section: Introductionmentioning

confidence: 99%

“…In shallow water environments, acoustic mode shapes (Brown et al, 2016;Zang et al, 2015), bottom properties (T. Tan et al, 2018;T. W. Tan et al, 2019T. W. Tan et al, , 2020, sound speed (F. Li et al, 2019), and flow velocity (Godin et al, 2014) have been estimated.…”

Section: Introductionmentioning

confidence: 99%

Using Ocean Ambient Sound to Measure Local Integrated Deep Ocean Temperature

Ragland,

Abadi,

Sabra

2024

Geophysical Research Letters

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Measuring the temperature changes of the deep ocean will be critical to understanding how the earth system will respond to climate change. In this work, we present a method for measuring the depth‐averaged, deep ocean temperature at local (∼3 km) spatial scales using passive estimates of acoustic propagation. These passive acoustic estimates of deep ocean temperature can be used with existing and future passive acoustic monitoring infrastructure to provide complimentary observations of the ocean to in situ measurements, and could be particularly useful in areas of poor ocean observation coverage. Using 8 years of ambient sound data, we demonstrate that the passive estimates agree with global ocean models and measurements by ARGO floats. The rms difference between the HYCOM ocean model is shown to be 0.13°C, and the rms difference between ARGO measurements is shown to be 0.086°C.

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“…The sound speed profile (SSP) plays an important role in civil and military marine applications. As it is a key parameter that determines the characteristics of sound transmission, accurately measuring the SSP is critical for such acoustic applications as localization [1], geoacoustic inversion [2], and tomography [3]. The perturbation-based monitoring of the SSP is the main means of acoustically monitoring the ocean.…”

Section: Introductionmentioning

confidence: 99%

Estimating Sound Speed Profile by Combining Satellite Data with In Situ Sea Surface Observations

Wang³

et al. 2022

Electronics

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Given that spatiotemporal measurement of the subsurface profile over a wide range are difficult to obtain, surface observations from satellites are often used to estimate the sound speed profile (SSP). This paper proposes a multisource method based on the self-organizing map (SOM) to improve the estimation of the SSP by merging surface observations with satellite data. Surface observations from the Kuroshio Extension Observatory (KEO) were used to supplement satellite observations (anomalies in the measured sea level and sea surface temperature) to this end. Different combinations of the surface parameters were assessed, their errors were analyzed, and differences between the results before and after the multisource parameters were used are discussed. The proposed method significantly increased the accuracy of estimating the SSP when the parameters obtained from in situ measurements were used, with a root mean square error of 2.18 m/s, less than a third of the error obtained when only satellite observations were used. The proposed method provides a new approach to determining an accurate three-dimensional structure of the sound speed when various surface observations are available.

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Passive geoacoustic inversion in the Mid-Atlantic Bight in the presence of strong water column variability

Cited by 11 publications

References 28 publications

A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric

A Multi-Objective Geoacoustic Inversion of Modal-Dispersion and Waveform Envelope Data Based on Wasserstein Metric

Using Ocean Ambient Sound to Measure Local Integrated Deep Ocean Temperature

Estimating Sound Speed Profile by Combining Satellite Data with In Situ Sea Surface Observations

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