Multi-resolution clustering analysis and 3-D visualization of multitudinous synthetic earthquakes View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2003

AUTHORS

Witold Dzwinel, David Yuen, Yoshihiro Kaneko, Krzysztof Boryczko, Yehuda Ben-Zion

ABSTRACT

We use modern and novel techniques to study the problems associated with detection and analysis of multitudinous seismic events, which form the background for isolated great earthquakes. This new approach involves multivariate analysis of low and large magnitude events recorded in space over a couple of centuries in time. We propose here the deployment of the clustering scheme both for extracting small local structures and large-scale trends in synthetic data obtained from four numerically simulated models with: uniform properties (U), a Parkfield-type asperity (A), fractal brittle properties (F), and multi-size-heterogeneity fault zone (M). The mutual nearest neighbor (mnn) clustering scheme allows for extraction of multi-resolutional seismic anomalies in both the spatio-temporal and multi-dimensional feature space. We demonstrate that the large earthquakes are correlated with a certain pathway of smaller events. Visualization of the anomalies by using a recently introduced visualization package Amira reveals clearly the spatio-temporal relationships between clusters of small, medium and large earthquakes, indicating significant stress relaxation across the entire fault region. We demonstrate that this mnn scheme can extract distinct clusters of the smallest events, which precede and follow a singularly large magnitude earthquake. These clusters form larger spatio-temporal structures comprising a series of large earthquakes. The link between the large and medium magnitude events is not so clearly understood. Short-ranged correlations are dominated by strong spatio-temporal anomalies, thus reflecting the global seismic properties of the entire fault zone. More... »

PAGES

1-32

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10069-002-0006-2

DOI

http://dx.doi.org/10.1007/s10069-002-0006-2

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1029482654


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