Bayesian event categorization matrix approach for explosion monitoring

Journal Article · Sat May 10 00:00:00 EDT 2025 · Geophysical Journal International

DOI:https://doi.org/10.1093/gji/ggaf171· OSTI ID:2570462

Koermer, Scott Carl ^[1]; Carmichael, Joshua Daniel ^[1]; Williams, Brian J. ^[1]

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1. Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)

Current efforts to correctly categorize natural events from suspected explosion sources with data that is collected by ground- or space-based sensors presents historical challenges that remain unaddressed by the Event Categorization Matrix (ECM) model. Smaller historical events (lower yield explosions) may have data available from fewer measurement techniques than are available today, and therefore, a historical event record can lack a complete set of discriminants. The covariance structures can also differ between such observations of event (source-type) categories. Both obstacles are problematic for the classic ECM model. Our work addresses this gap and presents a Bayesian update to the previous ECM model, termed the Bayesian Event Categorization Matrix model, which can be trained on partial observations and does not rely on a pooled covariance structure. We further augment the ECM model with Bayesian Decision Theory so that false negative or false positive rates of an event categorization can be reduced in an intuitive manner. To demonstrate improved categorization rates for the Bayesian Event Categorization Matrix model, we compare an array of Bayesian and classic models with multiple performance metrics using Monte Carlo experiments. We use both synthetic and real data. Our Bayesian models show consistent gains in overall accuracy and lower false negative rates relative to the classic ECM model. Here, we propose future avenues to improve Bayesian Event Categorization Matrix models’ decision making and predictive capability.

• Research Organization: Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program; USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: 89233218CNA000001; AC52-06NA25396
• OSTI ID: 2570462
• Report Number(s): LA-UR--24-30189; 10.1093/gji/ggaf171; 1365-246X
• Journal Information: Geophysical Journal International, Journal Name: Geophysical Journal International Journal Issue: 1 Vol. 242; ISSN 0956-540X; ISSN 1365-246X
• Publisher: Oxford University Press
• Country of Publication: United States
• Language: English

Related Subjects

58 GEOSCIENCES
Bayesian inference
Monte Carlo methods
Probability distributions
Seismology
Statistical methods