Seismicity Pattern Recognition in the Sumatra Megathrust Zone Through Mathematical Modeling of the Maximum Earthquake Magnitude Using Gaussian Mixture Models.

The research area of the present study is the Sumatra megathrust zone, which can be partitioned into five segments based on the large earthquake sources, including the Aceh Andaman, Nias Simeulue, Mentawai Siberut, Mentawai Pagai, and Enggano segments. This work presents the recognition of seismicity patterns in the research area from January 1970 to December 2022 using segmental and zonal mathematical modeling of the annual maximum earthquake magnitude. To achieve this, we use two kinds of Gaussian mixture models: G-group Gaussian independent mixture models (G-group GMMs) and N-state Gaussian hidden Markov models (N-state GHMMs) to determine the appropriate probability density function of the seismicity data (ePDF). The fit model is selected based on the smallest Bayes information criterion. For the segment analysis, the results show that the ePDF of the Mentawai-Pagai segment fits the 2-state GHMM, whereas, for the four remaining segments, it tends to fit the 2-group GMM. Subsequently, for the zone analysis, the ePDF of the data fits the 2-state GHMM. Thus, from a segmental and zoning point of view, seismicity patterns fluctuate at two levels. From a seismic risk management aspect, these findings can be used to evaluate the risk vulnerability of an area to destructive earthquakes. That is, the patterns of seismicity sequences in all segments of the Sumatra megathrust zone all fluctuate within the range of moderate to strong earthquakes. Furthermore, the seismicity pattern in the Mentawai-Pagai segment and the Sumatra megathrust zone has Markov properties. [ABSTRACT FROM AUTHOR]