Hidden Markov Modelling of Sparse Time Series from Non-Volcanic Tremor Observations

Abstract

SummaryTremor activity has been recently detected in various tectonic areas world wide and is spatially segmented and temporally recurrent. We design a type of hidden Markov models to investigate this phenomenon, where each state represents a distinct segment of tremor sources. A mixture distribution of a Bernoulli variable and a continuous variable is introduced into the hidden Markov model to solve the problem that tremor clusters are very sparse in time. We applied our model to the tremor data from the Tokai region in Japan to identify distinct segments of tremor source regions and the results reveal the spatiotemporal migration pattern among these segments.