A coupled ETAS-I2GMM point process with applications to seismic fault detection
| Autor: | George Mohler, Murat Dundar, Yicheng Cheng |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Statistics and Probability Computer science Kernel density estimation Mixture model Infinite Gaussian mixture model 01 natural sciences Fault detection and isolation Point process Physics::Geophysics 010104 statistics & probability 03 medical and health sciences 030104 developmental biology Modeling and Simulation Kernel (statistics) epidemic-type aftershock sequence 0101 mathematics Statistics Probability and Uncertainty Fault model Cluster analysis Algorithm Aftershock point process |
| Zdroj: | Ann. Appl. Stat. 12, no. 3 (2018), 1853-1870 |
| Popis: | Epidemic-type aftershock sequence (ETAS) point process is a common model for the occurrence of earthquake events. The ETAS model consists of a stationary background Poisson process modeling spontaneous earthquakes and a triggering kernel representing the space–time-magnitude distribution of aftershocks. Popular nonparametric methods for estimation of the background intensity include histograms and kernel density estimators. While these methods are able to capture local spatial heterogeneity in the intensity of spontaneous events, they do not capture well patterns resulting from fault line structure over larger spatial scales. Here we propose a two-layer infinite Gaussian mixture model for clustering of earthquake events into fault-like groups over intermediate spatial scales. We introduce a Monte Carlo expectation-maximization (EM) algorithm for joint inference of the ETAS-I2GMM model and then apply the model to the Southern California Earthquake Catalog. We illustrate the advantages of the ETAS-I2GMM model in terms of both goodness of fit of the intensity and recovery of fault line clusters in the Community Fault Model 3.0 from earthquake occurrence data. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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