Wishart Mixture-based Multiple Clustering for Selecting Seismic Stations for Low-frequency Earthquake Detection

Abstract

Clustering is an unsupervised learning method, which aims to classify objects according to differences in their underlying generative mechanisms. When classification predictions differ for objects depending on the selected features, a multiple clustering method which performs feature selection and object clustering simultaneously is particularly useful. In this paper, we focus on a specific type of clustering method (MCW), which performs multiple clustering for correlation matrices based on matrix partitioning. MCW is formalized as an extension of Wishart mixture models, and can identify multiple cluster solutions by inferring the block diagonalized structure of correlation matrices. In this study, we applied MCW to the seismic station selection problem for effectively detecting low-frequency earthquakes, which are local events occurring ubiquitously. To date, no solutions have been found for this problem. We applied MCW to the correlation matrices of spectrograms of low-frequency earthquake waveforms obtained from multiple seismic stations, which produced an optimal partitioning of seismic stations in terms of multiple clustering. As a result, a spatial correspondence between the selected seismic stations and the epicenters of low-frequency earthquakes was found, and the low-frequency earthquakes were classified into several clusters. Further, good reproducibility of the detection rate of low-frequency earthquakes was confirmed for specific selected seismic stations using validation data.