Clustering Earthquake Data

Abstract

Seismology, which is a sub-branch of geophysics, is one of the fields in which data mining methods can be effectively applied. In this chapter, employing data mining techniques on multivariate seismic data, decomposition of non-spatial variable is done. Then k-means clustering, density-based spatial clustering of applications with noise (DBSCAN), and hierarchical tree clustering algorithms are applied on decomposed data, and then pattern analysis is conducted using spatial data on the resulted clusters. The conducted analysis suggests that the clustering results with spatial data is compatible with the reality and characteristic features of regions related to earthquakes can be determined as a result of modeling seismic data using clustering algorithms. The baseline metric reported is clustering times for varying size of inputs.

Space-time clustering of seismic events in Chile using ST-DBSCAN-EV algorithm

“…The most popular is the K − M ean algorithm, which is based on the shortest distance between the data and the centroid ( [7]). In some specific cases this algorithm has been applied for the spatial clustering of earthquakes ( [8], [9]) however, recently, it is often considered as benchmark for assessing the performance of more advanced clustering techniques ( [10], [11], [12]). The main drawback of the k-means algorithm is that the optimal number of clusters is not automatically determined and different techniques could be considered for selecting this parameter ( [13]).…”

“…Fan et al [11] compared the results of K-means and DBSCAN, for clustering seismic events, showing the better performance of the DBSCAN algorithm. Savaş et al [12] described three principal techniques for clustering seismic events: the k-Means, DBSCAN and hierarchical tree clustering algorithms. By applying them to the spatial seismic events of the USGS they concluded that the results are compatible with the reality.…”