Optimal sampling strategies for seabed classification and source localization with Gaussian processes and machine learning

Abstract

Workshop ’97 data are employed for seabed classification and source range estimation. The data are acoustic fields computed at vertically separated receivers for various ranges and three different environments. Gaussian Processes are applied for denoising the data and predicting the field at virtual receivers, sampling the water column densely within the array aperture. The enhanced fields are then used in combination with machine learning in order to map the signals to one of 15 sediment-range classes (corresponding to three environments and five ranges). In prior work, the classification results after using Gaussian Processes for denoising were demonstrated to be superior to those when noisy workshop data are employed. Here, we explore optimal sampling strategies (e.g., nonuniform sampling, subsampling) for inducing sparsity in the correlation matrices that are based on hydrophone locations, and compare these with uniform sampling that was used in our prior work.