Bayesian Optimal Experiment Design for Analysis and Optimization of Seismo-Acoustic Monitoring Networks
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Speaker: Jake Callahan, Program in Applied Mathematics, University of Arizona
Abstract: Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. OED formulates the choice of experiment as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data.
We use Bayesian OED to find optimal sensor configurations for detecting seismic events as part of a seismic monitoring network. We develop the framework necessary to use Bayesian OED to optimize a sensor network's ability to locate seismic events from arrival time data of detected seismic phases.
Once we have developed this framework, we explore many relevant questions to monitoring such as how to trade off sensor fidelity and earth model uncertainty, how choice of prior distribution and domain restrictions affect sensor placement, and how sensor types, number, and locations influence uncertainty.