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Bayesian optimization with kernel ensembles and disagreement-based acquisition for source localization and acoustic inversion

arXiv · AI, language, vision and robotics · article · Sep 13, 2026 · UTC

Joint source localization and geoacoustic inversion requires optimizing an objective built from an expensive normal mode propagation model. Bayesian optimization (BO) with a Gaussian process (GP) surrogate can obtain accurate parameter estimates within a limited number of forward model evaluations, but its performance depends on the choice of kernel family. With few observations in a seven-dimensional search space, no single kernel can be expected to perform consistently well across individual inversions. To reduce this dependence, we use a weighted ensemble of GPs with different kernel famili

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