PROCEEDINGS of the23rd International Congress on Acoustics
9 to 13 September 2019 in Aachen, Germany

Bayesian geoacoustic inversion for SBCEX17 reflection, dispersion,and ship-noise data
Stan E. DOSSO 1; Charles W. HOLLAND 2; Julien BONNEL 3; Dag TOLLEFSEN4; Josee
BELCOURT1; Jan DETTMER5; David P. KNOBLES6123

School of Earth and Ocean Sciences, University of Victoria, Canada

Applied Physics Laboratory, The Pennsylvania State University, USA

Applied Ocean Physics and Engineering Department, Woods Hole Oceanographic Institution, USA45

Norwegian Defence Research Establishment, Norway

Department of Geoscience, University of Calgary, Canada
6Knobles Scientific and Analysis, USAABSTRACTThis paper considers a Bayesian inference approach to model selection, parameter estimation, anduncertainty quantification in seabed geoacoustic inversion, with application to three ocean acoustic data setsof different types collected as part of the 2017 Seabed Characterization Experiment (SBCEX17). The
inversion approach is based on trans-dimensional (trans-D) sampling, which considers the number of seabedlayers as an unknown parameter in the inversion. Trans-D sampling is carried out with the reversible jump
Markov chain Monte Carlo algorithm, which constructs a Markov chain that samples from the posterior
probability density (PPD) of the geoacoustic model parameters. Wide but efficient sampling of the trans-D
parameter space is achieved using principal-component reparameterization and parallel tempering. The
data-error model is based on the assumption of multivariate Gaussian errors with correlations represented byan autoregressive process—trans-D sampling of zeroth- and first-order autoregressive processes is applied to
avoid over- or under-parameterizing the error model. The Bayesian inference approach is applied to threeacoustic data sets from the New England Mud Patch during SBCEX17, including wide-angle(spherical-wave) reflection coefficients collected using a moored receiver and towed source; dispersion ofwater-borne acoustic modes using a single source and receiver, resolved by warping analysis; andmatched-field inversion of ship-of-opportunity noise recorded at a bottom-mounted horizontal receiver array.
Inversion results are presented in terms of marginal posterior probability profiles of sediment geoacoustic
parameters (sound speed, density, attenuation), quantifying parameter uncertainties. Results for inversions ofdifferent data types are found to be in good agreement with each other, and with other independent measuresof sediment properties, including cores and a high-resolution seismic survey. In all cases, the inversion results
indicate geoacoustic properties that are approximately constant with depth over an upper mud layer, with astrong (positive) sound-speed gradient at the base of this layer, consistent with increasing sand content.
Below this are higher-speed, higher-density layers, consistent with sand. Full results are presented in (1-3).
Keywords: Geoacoustic inversion, Bayesian inference, Sediment acoustics

ACKNOWLEDGEMENTS
This work was supported by the U.S. Office of Naval Research and the Canadian Department ofNational Defence.
1

sdosso@uvic.ca; josebelcourt@uvic.ca
cwh1@psu.edu
3
jbonnel@whoi.edu
4
dag.tollefsen@ffi.no5
jan.dettmer@ucalgary.ca
6
dpknobles@kphysics.org
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REFERENCES
1. Belcourt J, Holland CW, Dosso SE, Dettmer J. Goff J. Depth-dependent geoacoustic inferences withdispersion at the New England Mud Patch via reflection coefficient inversion. IEEE J Oceanic Eng.
2019:1-23.
2. Bonnel J, Dosso SE, Eleftherakis D, Chapman NR. Trans-dimensional inversion of modal dispersion
data on the New England Mud Patch. IEEE J Oceanic Eng. 2019:1-15.
3. Tollefsen D, Dosso SE, Knobles DP. Ship-of-opportunity noise inversions for geoacoustic profiles of alayered mud-sand seabed. IEEE J Oceanic Eng. 2019:1-12.

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