Original Article
Hidden Markov modelling of sparse time series from non‐volcanic tremor observations
Summary
Tremor 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.
Citing Literature
Number of times cited according to CrossRef: 4
- Jodie Buckby, Ting Wang, Jiancang Zhuang, Kazushige Obara, Model Checking for Hidden Markov Models, Journal of Computational and Graphical Statistics, 10.1080/10618600.2020.1743295, (1-16), (2020).
- P. Bountzis, E. Papadimitriou, G. Tsaklidis, Earthquake clusters identification through a Markovian Arrival Process (MAP): Application in Corinth Gulf (Greece), Physica A: Statistical Mechanics and its Applications, 10.1016/j.physa.2019.123655, (2019).
- Guglielmo D’Amico, Ada Lika, Filippo Petroni, Change point dynamics for financial data: an indexed Markov chain approach, Annals of Finance, 10.1007/s10436-018-0337-0, (2018).
- P. Bountzis, E. Papadimitriou, G. Tsaklidis, Estimating the earthquake occurrence rates in Corinth Gulf (Greece) through Markovian arrival process modeling, Journal of Applied Statistics, 10.1080/02664763.2018.1531977, (1-26), (2018).
