文档介绍：Cluster Analysis, Data-Mining, Multi-dimensional Visualization of
Earthquakes over Space, Time and Feature Space
Witold Dzwinel1, David ,
Krzysztof Boryczko1,2, Yehuda Ben-Zion3, Shoichi Yoshioka4, Takeo Ito5
1AGH Institute puter Science, al. Mickiewicza 30, 30-059, Kraków, Poland
2 Minnesota puting Institute, Univ. of Minnesota, Minneapolis, MN 55455, USA
3Department of Earth Sciences, University of Southern California, Los Angeles, CA 98809, USA
4 Dept. of Earth and Planteary Sciences, Kyushu University, Fukuoka, 812-8581, Japan
5 Graduate School of Environmental Studies, Nagoya, University, Furo-cho, Nagoya, Aichi, 464-8602, Japan
Abstract
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 multi-
dimensional 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 t