Author:
Avi Bleiweiss
Affiliation:
Intel Corporation, United States
Keyword(s):
Earthquake, Seismic, Mixture Model, Expectation-maximization, Hidden Markov Model, Clustering.
Abstract:
Traditionally, earthquake events are identified by prescribed and well formed geographical region boundaries.
However, fixed regional schemes are subject to overlook seismic patterns typified by cross boundary relations
that deem essential to seismological research. Rather, we investigate a statistically driven system that clusters
earthquake bound places by similarity in seismic feature space, and is impartial to geo-spatial proximity
constraints. To facilitate our study, we acquired hundreds of thousands recordings of earthquake episodes that
span an extended time period of forty years, and split them into groups singled out by their corresponding
geographical places. From each collection of place affiliated event data, we have extracted objective seismic
features expressed in both a compact term frequency of scales format, and as a discrete signal representation
that captures magnitude samples in regular time intervals. The distribution and temporal typed feature vectors
are furth
er applied towards our mixture model and Markov chain frameworks, respectively, to conduct clustering
of shake affected locations. We performed extensive cluster analysis and classification experiments, and
report robust results that support the intuition of geo-spatial neutral similarity.
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