A Bayesian geoacoustic inversion based on modal dispersions of water wave and ground wave

Abstract

Most shallow water geoacoustic inversions based on modal dispersion cannot reliably estimate the deep geoacoustic parameters. Because these studies focus on the dispersions of water waves but ignore the dispersions of ground waves. Therefore, this paper studied a Bayesian geoacoustic inversion based on wideband modal dispersions of water waves and ground waves. Firstly, the modal dispersion curves with Airy phase components were discussed. Secondly, the Bayesian inversion theory and a novel sample-efficient inference algorithm, namely Variational Bayesian Monte Carlo, were introduced briefly. In the Bayesian inversion, the posterior probability densities of unknown parameters are inferred, which can provide the prediction closest to the observation data and the uncertainty of the prediction. Considering that the forward acoustic model is computationally intensive, the posterior analysis is carried out by using the Variational Bayesian Monte Carlo method. It is performed by finding the variational distribution closest to the target distribution and requires less computation time than the Markov chain Monto Carlo method. In the simulation study, a range-independent two-layer seabed, including the sediment layer and basement layer, is modeled, assuming that the water column is homogeneous. The function of shear wave in waveguide was ignored. The compressional sound speed of the sediment layer varied linearly from c1U to c1L between 0 and h1, while other geoacoustic parameters were constant. By comparing the inversion results with and without the information of ground waves for different signal-to-noise ratios, it can be concluded that the deep geoacoustic parameters are more sensitive to the dispersions of ground waves. And then, a shallow-water experimental study was carried out in the Bohai Sea of China. The average water depth was about 20m. The wideband pulse signals were recorded by a hydrophone with a sensitivity of -170dB re 1V/μPa. The received signals included well-defined Airy phase components, and the modal dispersion curves of water waves and ground waves were extracted accurately. The experimental results indicated that the Bayesian inversion combining water and ground wave dispersions can not only estimate the deep geoacoustic parameters reliably, but also reduce the inversion uncertainties of other model parameters, such as the shallow geoacoustic parameters, water depth, and so on. The estimated source-receiver range and water sound speed are close to their measured values. The modal dispersion curves predicted by the posterior mean samples were in good agreement with those extracted from the experimental data at different ranges. In addition, the well forecast of transmission loss also demonstrated the reliability of the joint inversion.