Analysis and Optimization of Seismic Monitoring Networks with Bayesian Optimal Experiment Design

• URL: http://arxiv.org/abs/2410.07215v2
• Date: Mon, 14 Oct 2024 19:47:12 GMT
• Title: Analysis and Optimization of Seismic Monitoring Networks with Bayesian Optimal Experiment Design
• Authors: Jake Callahan, Kevin Monogue, Ruben Villarreal, Tommie Catanach,
• Abstract summary: Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem. In this work, we develop the framework necessary to use Bayesian OED to optimize a sensor network's ability to locate seismic events.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Monitoring networks increasingly aim to assimilate data from a large number of diverse sensors covering many sensing modalities. Bayesian optimal experimental design (OED) seeks to identify data, sensor configurations, or experiments which can optimally reduce uncertainty and hence increase the performance of a monitoring network. Information theory guides OED by formulating the choice of experiment or sensor placement as an optimization problem that maximizes the expected information gain (EIG) about quantities of interest given prior knowledge and models of expected observation data. Therefore, within the context of seismo-acoustic monitoring, we can use Bayesian OED to configure sensor networks by choosing sensor locations, types, and fidelity in order to improve our ability to identify and locate seismic sources. In this work, 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 at the regional-scale. Bayesian OED requires four elements: 1) A likelihood function that describes the distribution of detection and travel time data from the sensor network, 2) A Bayesian solver that uses a prior and likelihood to identify the posterior distribution of seismic events given the data, 3) An algorithm to compute EIG about seismic events over a dataset of hypothetical prior events, 4) An optimizer that finds a sensor network which maximizes EIG. 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 sensor types, number, and locations influence uncertainty; and how prior models and constraints influence sensor placement.

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