Identifying spatiotemporal migration patterns of non-volcanic tremors using hidden Markov models

Abstract:

Tremor activity has been recently detected in various tectonic areas worldwide, and is spatially segmented and temporally recurrent. We design a type of hidden Markov models (HMMs) to investigate this phenomenon, where each state represents a distinct segment of tremor sources. We systematically analyze the tremor data from the Tokai region in southwest Japan using this model and find that tremors in this region concentrate around several distinct centers. We find: (1) The system is classified into three classes, background (quiescent), quasi-quiescent, and active states; (2) The region can be separated into two subsystems, the southwest and northeast parts, with most of the active transitions being among the states in each subsystem and the other transitions mainly to the quiescent/quasi-quiescent states; and (3) Tremor activity lasts longer in the northeastern part than in the southwest part. The success of this analysis indicates the power of HMMs in revealing the underlying physical process that drives non-volcanic tremors.

Figure： The migration pattern for the HMM with 8 states. Top panel: Observed distances with the center μ_i of each state overlayed as the red line and ±σ_i on the left-hand side of the panel in green lines; Middle panel: the tracked most likely state sequence of the 8-state HMM; Bottom panel: the estimated probability of the data being in each state, with blank representing the probability of being in State 1 (the null state).