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Bayesian Optimal Experimental Design With Constraints for Seismo-acoustic Sensor Networks

Description: 

Bayesian optimal experimental design (OED) offers a principled framework for designing sensor networks that maximize the information gain about critical monitoring objectives, such as event location and magnitude. Our presentation will address two significant challenges in Bayesian OED that arise in realistic monitoring scenarios: the impact of emplacement constraints and sensor budgets on network performance, and the integration of heterogeneous sensors differing in fidelity and sensing modality (e.g. seismic vs. infrasound). These complexities require the development of advanced optimization methods to enhance our OED workflow, ensuring robust and efficient sensor network design. We demonstrate our approach using a local seismic monitoring case study in Nevada, leveraging existing sensor networks and well characterized earth models to explore the proposed methods.

This Low Yield Nuclear Monitoring (LYNM) 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, NNSS, PNNL, and SNL.

Session: Advancements in Forensic Seismology and Explosion Monitoring - I

Type: Oral

Date: 4/17/2025

Presentation Time: 08:15 AM (local time)

Presenting Author: Tommie

Student Presenter: No

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