Two-State Poisson Hidden Markov Models for Analysis of Seismicity Activity Rates in West Nusa Tenggara

9th Annual Basic Science International Conference 2019

自然科学（总论）

Azizah, Nur^1 ; Astutik, Suci^1 ; Nurjannah^1

Department of Statistics, Faculty of Mathematics and Natural Sciences, Brawijaya University, Malang, Indonesia^1

关键词: Bayesian approaches;    Dependency relationship;    Earthquake events;    Long-run behavior;    Mean absolute error;    Model validation;    Parameter estimation method;    United states geological surveys;   

Others  :  https://iopscience.iop.org/article/10.1088/1757-899X/546/5/052015/pdf
DOI  :  10.1088/1757-899X/546/5/052015

学科分类：自然科学（综合）

来源: IOP

【 摘 要 】

Awareness of seismicity activity rates could be learned from modeling the earthquake events by utilizing the record of seismicity events data in NTB over time which is associated with count time series data. Poisson Hidden Markov Model (PHMM) has been widely applied in various fields, including earthquake event. Therefore, it would be interesting to implement PHMM on earthquake case in NTB. The data can be analyze using PHMM as we can ignore the over-dispersion and dependency relationship among data. The model is the development of Markov Model that consists of (a) observed state, which can be observed directly and (b) hidden state, which cannot be observed directly because it is hidden. Hidden state in this research is defined as seismicity activity rates classified into a low rate and high rate (2 states). The count time series data of earthquake events will be more informative when it is classified into the seismicity activity levels. This research applied earthquake event data (magnitude ≤ M4.7 and depth