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Bayesian Optimal Experimental Design for Seismic and Infrasound Monitoring Networks

Description: 

The goal of Bayesian optimal experimental design (OED) is to find an experiment (e.g. the data gathering and processing methods) that maximizes the expected information gained about quantities of interest given prior knowledge and models of the environment. Within the context of monitoring, we can use Bayesian OED to evaluate and optimize sensor configurations for seismic and infrasound stations. This could mean choosing the station phenomenology, fidelity, location, sensor type, and outputs in order to optimize learning an event’s characteristics such as location and/or magnitude. By developing Bayesian OED tools for analyzing and designing monitoring networks, we can explore many relevant questions such as: how can we combine data phenomenologies to reduce uncertainty, how much is gained by reducing sensor noise or earth model uncertainty, and how do sensor types, number, and locations influence uncertainty.

First, we will describe the general background theory of Bayesian OED and how to use it to formalize the study of monitoring networks. Then, we will discuss our OED framework and our associated source and propagation models that we can use to answer these types of questions in the context of seismic and infrasound stations. We will also demonstrate how our framework allows specifying prior information about sources, constraints on sensor placement locations and how these assumptions can significantly alter the optimal sensor configuration.

This research was funded by the National Nuclear Security Administration, Defense Nuclear Nonproliferation Research and Development (NNSA DNN R&D). The authors acknowledge important interdisciplinary collaboration with scientists and engineers from LANL, LLNL, MSTS, PNNL, and SNL. SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525.

Session: Exploiting Explosion Sources: Advancements in Seismic Source Physics [Poster]

Type: Poster

Date: 4/19/2023

Presentation Time: 08:00 AM (local time)

Presenting Author: Tommie A. Catanach

Student Presenter: No

Invited Presentation: 

Authors