Identifying the Recurrence Patterns of Nonvolcanic Tremors Using a 2‐D Hidden Markov Model With Extra Zeros

Abstract

Nonvolcanic tremor activity has been observed in many places worldwide. In some regions, their activity was observed to accompany slow slip events. Before examining whether and how nonvolcanic tremor activity is related to slow slip, it is essential to understand quantitatively the spatiotemporal migration patterns of nonvolcanic tremors. We developed a 2‐D hidden Markov model to automatically analyze and forecast the spatiotemporal behavior of tremor activity in the regions Kii and Shikoku, southwest Japan. This new automated procedure classifies the tremor source regions into distinct segments in 2‐D space and infers a clear hierarchical structure of tremor activity, where each region consists of several subsystems and each subsystem contains several segments. The segments can be quantitatively categorized into three different types according to their occurrence patterns: episodic, weak concentration, and background, extending earlier knowledge gained from handpicked tremor swarms. The Kii region can be categorized into four different subsystems, with two often linked to each other. The Shikoku region can be divided into six subsystems, with two in central Shikoku linked to each other. Moreover, a significant increase in the proportion of tremor occurrence was detected in a segment in southwest Shikoku before the 2003 and 2010 long‐term slow slip events in the Bungo channel. This highlights the possible correlation between nonvolcanic tremor and slow slip events. The model can be used to analyze tremor data from other regions.