Nonstationary ETAS models for nonstandard earthquakes
Takao Kumazawa, Yosihiko Ogata

TL;DR
This paper introduces nonstationary extensions of the ETAS model to better fit nonstandard earthquake swarms caused by transient stress changes, using empirical Bayes estimation and demonstrated on a 2011 Tohoku-Oki swarm.
Contribution
It develops time-dependent parameter models for nonstandard earthquakes and applies empirical Bayes for robust estimation, improving fit over stationary models.
Findings
Nonstationary ETAS models better fit transient earthquake swarms.
Empirical Bayes method provides robust parameter estimation.
Model successfully applied to 2011 Tohoku-Oki earthquake swarm.
Abstract
The conditional intensity function of a point process is a useful tool for generating probability forecasts of earthquakes. The epidemic-type aftershock sequence (ETAS) model is defined by a conditional intensity function, and the corresponding point process is equivalent to a branching process, assuming that an earthquake generates a cluster of offspring earthquakes (triggered earthquakes or so-called aftershocks). Further, the size of the first-generation cluster depends on the magnitude of the triggering (parent) earthquake. The ETAS model provides a good fit to standard earthquake occurrences. However, there are nonstandard earthquake series that appear under transient stress changes caused by aseismic forces such as volcanic magma or fluid intrusions. These events trigger transient nonstandard earthquake swarms, and they are poorly fitted by the stationary ETAS model. In this…
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