Hybrid Computational Cluster for Ocean Acoustic Inversion

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Acoustic remote sensing of seabed properties and of sources of sound in the ocean are important problems with geological, geotechnical, marine-mammal, and defence (sonar) applications. However, these applications require formulating and solving nonlinear inverse problems. Although often neglected due to computational complexity, quantitative uncertainty estimation and model selection (e.g., determining the number of seabed sediment layers or vocalizing marine mammals consistent with the data) are key components of meaningful inversion analysis. We are developing advanced algorithms for model selection and uncertainty estimation in ocean acoustic and seismic inversion. These algorithms numerically sample the posterior probability density (the probability distribution of the model parameters given the observed data and prior information), and include probabilistic sampling over the choice of model. While our algorithms are general and powerful, they are computationally intensive such that our current work, which employs multiple parallel samplers on a central-processing unit (CPU) cluster, is strongly compute-limited. However, the recent application of graphics processing units (GPUs) in computational problems has the potential to revolutionize nonlinear (numerical) inversion. In particular, the fine-grained parallelism of GPU coding for computational data predictions complements the coarse-grained (CPU) parallelism of multiple samplers in the inversion; combining these in an integrated hybrid computing facility represents novel hardware ideally suited to our nonlinear inference approaches. Acquisition of a hybrid GPU/CPU system would allow our research to reach its full potential, with applications to large and sequential data sets, full-wave inversion problems, localizing an unknown number of sources in a poorly-known environment, and in tomographic inversion for seabed/earth models that vary in two dimensions (vertically and laterally). Acoustic/seismic data and experiments for these applications are facilitated through ongoing collaborations with the Geological Survey of Canada, Ocean Networks Canada, local marine-technology industry, and international institutions.