Automated Geoacoustic Inversion and Uncertainty: Meso-scale Seabed Variability in Shallow Water Environments
VICTORIA UNIV (CANADA) SCHOOL OF EARTH AND OCEAN SCIENCES
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Propagation and reverberation of acoustic fields in shallow water depend strongly on the spatial variability of seabed geoacoustic parameters, and lack of knowledge of seabed variability is often a limiting factor in acoustic modeling applications. However, direct sampling e.g., coring of vertical and lateral variability is expensive and laborious, and long-range inversion methods can fail to provide sufficient resolution. For proper quantitative examination of variability, parameter uncertainty must be quantified first which can be particularly challenging for large data sets, and in range-dependent andor dispersive seabed environments. A long-term goal of this work is to substantially advance Bayesian inversion methodology to allow automated analysis of large and complex data sets. These advances will allow mesoscale spatial variability of seabed sediments to be quantified in two and three dimensions. In addition, more detailed understanding of acoustic propagation in porous sediments is desirable. For example, understanding acoustic dispersion in seabed sediments is of significant interest to the acoustical oceanography community. Inferring such complex quantities from acoustic measurements also requires a higher level of sophistication in modeling the seabed, for example by accounting for shear and scattering in arbitrarily layered seabeds. Obtaining meaningful inferences on low-frequency dispersion is a challenging inverse problem since estimates can strongly depend on the spatial structure layering of the sediment and multiple competing physical theories exist that can predict similar dispersion regimes. Further, direct sampling e.g., laboratory measurements of core properties is currently not possible for low frequencies hundreds of hertz.
- Physical and Dynamic Oceanography
- Geology, Geochemistry and Mineralogy