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Nonlinear Processes in Geophysics An interactive open-access journal of the European Geosciences Union
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Volume 12, issue 1
Nonlin. Processes Geophys., 12, 117–128, 2005
https://doi.org/10.5194/npg-12-117-2005
© Author(s) 2005. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.
Nonlin. Processes Geophys., 12, 117–128, 2005
https://doi.org/10.5194/npg-12-117-2005
© Author(s) 2005. This work is licensed under
the Creative Commons Attribution-NonCommercial-ShareAlike 2.5 License.

  28 Jan 2005

28 Jan 2005

Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space

W. Dzwinel1, D. A. Yuen2, K. Boryczko1, Y. Ben-Zion3, S. Yoshioka4, and T. Ito5 W. Dzwinel et al.
  • 1AGH Institute of Computer Science, al. Mickiewicza 30, 30-059, Kraków, Poland
  • 2Minnesota Supercomputing Institute, Univ. of Minnesota, Minneapolis, MN 55455, USA
  • 3Department of Earth Sciences, University of Southern California, Los Angeles, CA 90089, USA
  • 4Department of Earth and Planetary Sciences, Kyushu University, Fukuoka, 812-8581, Japan
  • 5Graduate School of Environmental Studies, Nagoya, University, Furo-cho, Nagoya, Aichi, 464-8602, Japan

Abstract. We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997-2003. 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 structures 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 delineating neighboring large events. Seismic events, quantized in space and time, generate the multi-dimensional feature space characterized by the earthquake parameters. Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters. At the spatial resolutions characteristic of the earthquake parameters, all of the ongoing seismicity both before and after the largest events accumulates to a global structure consisting of a few separate clusters in the feature space. We show that by combining the results of clustering in both low and high resolution spaces, we can recognize precursory events more precisely and unravel vital information that cannot be discerned at a single resolution.

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