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

2026-09-12

AI Summary

研究使用带核集成和基于分歧的获取函数的贝叶斯优化方法，解决源定位和声学反演问题，通过加权集成不同核族的高斯过程提高性能。

Abstract

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 families, allowing the surrogate to adapt to the observed objective without committing to one kernel in advance. The ensemble is combined with an optimum-conditioned acquisition function that determines where the expensive objective should be evaluated next. Experiments on simulated and measured SWellEx-96 data show that the resulting method achieves the lowest mean final objective among the considered BO strategies and reduces parameter estimation error on most coordinates. Ablation results further show that the ensemble provides robustness to kernel choice, while the acquisition function accounts for most of the optimization gain.

Problem

• Bayesian optimization
• kernel ensemble
• source localization
• acoustic inversion
• Gaussian process

Innovation

• Bayesian Optimization
• Gaussian Process Ensemble
• Kernel Families
• Optimum-Conditioned Acquisition Function
• Source Localization