High-performance Geoacoustic inversion cluster

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HIGH-PERFORMANCE GEOACOUSTIC INVERSION CLUSTER: Seabed geoacoustic parameters, such as sediment layering structure, sound speed, density, attenuation and scattering strength, strongly affect the propagation of ocean acoustic fields. Hence, knowledge of these parameters is needed to understand and model sound propagation in shallow-water environments, as required for many sonar applications (as well as geotechnical and geological purposes). Estimating seabed parameters from ocean acoustic measurements provides a convenient in situ alternative to direct sampling (e.g., coring), with a sensitivity relevant to sonar. However, this requires solving a strongly nonlinear inverse problem that is inherently nonunique (i.e., a range of solutions can acceptably fit the data). Quantitative uncertainty estimation and model selection (e.g., determining appropriate seabed parameterizations) are key components of meaningful inversion analysis, but are often neglected due to the high computational effort required for nonlinear problems. We are developing a Bayesian inference formulation and a numerical (statistical) sampling approach that builds up probability distributions for the unknown parameters and evaluates evidence for model selection based on the data and physical constraints (prior information). This approach is rigorous and fully nonlinear, but computationally intensive such that computing capacity currently represents the limiting factor in our research. Our inversion algorithms have been/are being developed to exploit parallel processing on multiple compute nodes, with low memory and inter-node communication requirements. The new generation of relatively inexpensive, massively-parallel machines, based on multi-core processors, are ideally suited to such applications. Acquisition of a massively parallel computer would allow our research to reach its full potential, with applications such as geaocoustic inversion of full-field acoustic measurements, acoustic reverberation, seabed reflectivity, ambient noise, and ship noise, as well as transfering the inversion methodologies to geophysical inverse problems such as magnetotellurics, surface-wave dispersion, and receiver function analysis.