Cluster Analysis, Data-Mining, Multi-dimensional Visualization of Earthquakes over Space, Time and Feature Space

A novel technique based on cluster analysis of the multi-resolutional structure of earthquake patterns is developed and applied to observed and synthetic seismic catalogs. The observed data represent seismic activities situated around the Japanese islands in the 1997-2003 time interval. The synthetic data were generated by numerical simulations for various cases of a heterogeneous fault governed by 3-D elastic dislocation and power-law creep. At the highest resolution, we analyze the local cluster structure in the data space of seismic events for the two types of catalogs by using an agglomerative clustering algorithm. We demonstrate that small magnitude events produce local spatio-temporal patches corresponding to neighboring large events. Seismic events, quantized in space and time, generate the multi-dimensional feature space of the earthquake parameters. Using a non-hierarchical clustering algorithm and multidimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of multivariate clusters. At the resolutions characteristic of the earthquake parameters, all of the ongoing seismicity before and after largest events accumulate to a global structure consisting of a few separate clusters in the feature space. We show that by combining the clustering results from low and high resolution spaces, we can recognize precursory events more precisely and decode vital information that cannot be discerned at a single level of resolution.