A matrix-variate dirichlet process to model earthquake hypocentre temporal patterns. (August 2022)

• Record Type: Journal Article
• Authors: A. Ray, Meredith; Bowman, Dale; Csontos, Ryan; Van Arsdale, Roy B.; Zhang, Hongmei
• Is Part Of: Statistical modelling. Volume 22:Number 4(2022)
• Page Start: 245
• Page End: 272
• Publication Date: 2022-08
• Subjects: Bayesian -- Dirichlet process -- Earthquake -- hypocentres -- spatial clustering; Linear models (Statistics) -- Periodicals; Mathematical models -- Periodicals; Modèles linéaires (Statistique) -- Périodiques; Modèles mathématiques -- Périodiques; Modèle statistique; Modèle linéaire; Modélisation statistique; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 519.5011
• DOI: 10.1177/1471082X20939767
• Languages: English
• ISSNs: 1471-082X

Abstract

Earthquakes are one of the deadliest natural disasters. Our study focuses on detecting temporal patterns of earthquakes occurring along intraplate faults in the New Madrid seismic zone (NMSZ) within the middle of the United States from 1996–2016. Based on the magnitude and location of each earthquake, we developed a Bayesian clustering method to group hypocentres such that each group shared the same temporal pattern of occurrence. We constructed a matrix-variate Dirichlet process prior to describe temporal trends in the space and to detect regions showing similar temporal patterns. Simulations were conducted to assess accuracy and performance of the proposed method and to compare to other commonly used clustering methods such as Kmean, Kmedian and partition-around-medoids. We applied the method to NMSZ data to identify clusters of temporal patterns, which represent areas of stress that are potentially migrating over time. This information can then be used to assist in the prediction of future earthquakes.