Abstract
A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non- Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.
| Original language | English |
|---|---|
| Pages (from-to) | 1750-1764 |
| Number of pages | 15 |
| Journal | Geophysical Journal International |
| Volume | 221 |
| Issue number | 3 |
| DOIs | |
| State | Published - 2020 |
| Externally published | Yes |
Funding
We gratefully acknowledge the support of Ms Leslie Casey and the National Nuclear Security Administration Office of Nonproliferation and Treaty Verification Research and Development for funding the research discussed here. Los Alamos National Laboratory completed this work under the auspices of the U.S. Department of Energy.
Keywords
- Acoustic properties
- Earthquake monitoring and test-ban treaty verification
- Probability distributions
- Statistical methods
- Wave propagation