Bayesian environmental inversion of airgun modal dispersion using a single hydrophone in the Chukchi Sea

Warner, Graham A.; Dosso, Stan E.; Dettmer, Jan; Hannay, David

doi:10.1121/1.4920579

Cited by 10 publications

(10 citation statements)

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“…In our view, it seems interesting to find out, how many grid points are required to resolve the function c(z) and how accurate the inversion result can be. In [25] a similar question was studied by using a sophisticated trans-D Bayesian inversion approach. Our results are obtained in a much more simple and straightforward way, and they are also probably easier to understand.…”

Section: Discussionmentioning

confidence: 99%

“…The results from [25] show that certain approximation of the function c(z) can be obtained by the dispersion-based inversion (although in [25] it is not really perfect even for synthetic data). In the present work we show that at least in principle it can be inverted for very accurately (when inverting for the sound speed profile alone and considering all other parameters known).…”

Section: Waveguide Model and The Synthetic Datamentioning

confidence: 99%

“…The results for Nc = 6 also indicate that the increase of the number of nodes does not necessarily improves the inversion accuracy. This can be considered an additional justification for using trans-D inversion technique [25], where the preference in given to the sound speed profile approximations with fewer nodes.…”

Section: Computational Experiments In Acoustics@homementioning

confidence: 99%

“…The dispersion-based technique for the geoacoustic waveguide parameters estimation [9] introduced by J. Bonnel and his colleagues several years ago is currently a very dynamic field of research in underwater acoustics [8,10,23,25]. In this technique, the dispersion data is used as an input of the inversion algorithm.…”

Section: Dispersion-based Geoacoustic Inversionmentioning

confidence: 99%

“…Due to the lack of the spatial diversity in single-hydrophone measurements, the inversion procedure in this case relies on the so-called waveguide dispersion, i.e., the dependence of arrival times on frequency and mode number (see details, e.g., in [9,23]). Some interesting recent results [25] also indicate that single-hydrophone dispersion-based inversion method outlined in [9] can be used for the estimation of a sound-speed profile in a shallow-water waveguide. The implementation of this method in practice can be thought of as a solution of a minimization problem in a (very large) discrete search space [26], and every evaluation of the cost function requires numerous solutions of an acoustic spectral problem [23,26].…”

Section: Introductionmentioning

confidence: 99%

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A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

et al. 2017

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Abstract:A volunteer computing project aimed at solving computationally hard inverse problems in underwater acoustics is described. This project was used to study the possibilities of the sound speed profile reconstruction in a shallow-water waveguide using a dispersion-based geoacoustic inversion scheme. The computational capabilities provided by the project allowed us to investigate the accuracy of the inversion for different mesh sizes of the sound speed profile discretization grid. This problem suits well for volunteer computing because it can be easily decomposed into independent simpler subproblems.

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Section: Discussionmentioning

confidence: 99%

Section: Waveguide Model and The Synthetic Datamentioning

confidence: 99%

Section: Computational Experiments In Acoustics@homementioning

confidence: 99%

Section: Dispersion-based Geoacoustic Inversionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

et al. 2017

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Taming uncertainty in geophysical inversion

Sambridge

Hawkins

Dettmer

2016

ASEG Extended Abstracts

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SUMMARYThe concept of uncertainty in geophysical inversion is often confined to quantification of errors in parameters estimated from some data. A broader definition is to include uncertainty arising from the assumptions made in posing the inverse problem in the first place. These may include assumptions about the physics of the relationship between observations and unknowns, the class of parameterisation assumed for the unknowns, and guesses about the statistical character of random noise contaminating the data. Typically these assumptions are required to arrive at a tractable mathematical problem to solve using geophysical inversion methods. In this paper we outline an inversion approach that allows a broader definition of uncertainty which includes each of these classes of assumption. Including uncertainty in the model parametrization or in the nature of the noise statistics can lead to more realistic inversion results, but not always with increased error bars on the model parameters. For example, relaxing rigid assumptions in the nature of the parametrisation, even in simple problems can result in smaller and more realistic model error estimation. Using the data to decide between different classes of parameterization, physical assumptions or observational noise process is often called `model choice' in statistics, an area that is often overlooked in the geosciences. Over the past 5 years the trans-dimensional inversion approach has increasingly found applications across a variety of inference problems in the geosciences, with new applications appearing regularly.

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Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

Bonnel¹,

Dosso²,

Eleftherakis³

et al. 2020

IEEE J. Oceanic Eng.

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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. Index Terms-Geoacoustic inversion, New England mud patch, seabed characterization experiment (SBCEX), underwater acoustics, warping.

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Bayesian environmental inversion of airgun modal dispersion using a single hydrophone in the Chukchi Sea

Cited by 10 publications

References 0 publications

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

A Volunteer Computing Project for Solving Geoacoustic Inversion Problems

Taming uncertainty in geophysical inversion

Trans-Dimensional Inversion of Modal Dispersion Data on the New England Mud Patch

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