Geoacoustic Inversion of Reflection Data in a Water Tank
This paper describes a nonlinear Bayesian approach to invert underwater acoustic reflection data for geoacoustic properties of sand layers at the base of a water tank. Recorded broadband acoustic signals (25-35 kHz) were processed by cross-correlating with the transmitted signal, and then windowed and filtered to estimate the reflection coefficient at multiple frequencies. In the Bayesian inversion, the unknown geoacoustic quantities of interest are considered as random variables constrained by data and prior information with the goal of interpreting the multi-dimensional posterior probability density. The inversion applies delayed rejection adaptive Metropolis (DRAM), a Markov Chain Monte Carlo sampling method, which not only provides a maximum a-posteriori parameters estimates but also quantifies the parameters uncertainties in terms of marginal probability distributions and credibility intervals; inter-parameter relationships are quantified by the correlation matrix and joint marginal distributions. Bayesian reflection inversion is illustrated for acoustic data measured in an anechoic water tank experiment conducted at Harbin Engineering University, China.
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