Spatio-Temporal Clustering of Earthquakes Based on Average Magnitudes

摘要

In this paper, we address the problem of automatically extracting several clusters consisting of spatio-temporally similar earthquakes whose average magnitudes are substantially different from the total average. For this purpose, we propose a new method consisting of two phases: tree construction and tree separation. In the former phase, we employ one of two different declustering algorithms called single-link and correlation-metric developed in the field of seismology, while in the later phase, we employ a variant of the change-point detection algorithm, developed in the field of data mining. In our empirical evaluation using earthquake catalog data covering the whole of Japan, we show that the proposed method employing the single-link algorithm can produce more desirable results for our purpose in terms of the improvement of weighted sums of variances and visualization results.