Abstract
Conventionally, earthquake events are recognized by guided and well established geographical region confines. However, explicit regional schemes are prone to overlook patterns manifested by cross-boundary seismic relations that are regarded vital to seismological research. Rather, we investigate a statistically motivated system that clusters earthquake impacted places by similarity in seismic feature space, and is hence impartial to geo-spatial proximity constraints. To facilitate our study, we have acquired hundreds of thousands recordings of earthquake episodes that traverse an extended time period of forty years. Episodes are split into groups singled out by their affiliated geographical place, and from each, 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 spaced in regular time intervals. Attribute vectors of the distributional and temporal domains are further applied towards our mixture model and Markov chain frameworks, respectively, to conduct clustering of presumed unlabeled, shake affected locations. We performed comprehensive cluster analysis and classification experiments, and report robust results that support the intuition of geo-spatial neutral similarity.
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References
Akaike, H.: Information theory and an extension of the maximum likelihood principle. In: International Symposium on Information Theory, Budapest, pp. 267-281 (1973)
Baeza-Yates, R., Ribeiro-Neto, B.: Modern Information Retrieval. ACM Press Series/Addison Wesley, Essex (1999)
Baum, L.E.: An inequality and associated maximization technique in statistical estimation for probabilistic functions of Markov processes. In: Symposium on Inequalities, Los Angeles, pp. 1-8 (1972)
Baum, L.E., Petrie, T.: Statistical inference for probabilistic functions of finite state Markov chains. Ann. Math. Stat. 37(6), 1554–1563 (1966)
Cormen, T.H., Leiserson, C.H., Rivest, R.L., Stein, C.: Introduction to Algorithms. MIT Press/McGraw-Hill Book Company, Cambridge (1990)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. J. R. Stat. Soc. 39(1), 1–38 (1977)
Duda, R.O., Hart, P.E., Stork, D.G.: Unsupervised learning and clustering. In: Pattern Classification, pp. 517–601. Wiley, New York (2001)
Flinn-Engdahl Seismic and Geographic Regionalization Scheme (2000). http://earthquake.usgs.gov/learn/topics/flinn_engdahl.php
Fraley, C., Raftery, A.E.: Bayesian regularization for normal mixture estimation and model-based clustering. J. Class. 24(2), 155–181 (2007)
Fraley, C., Raftery, A.E.: Model-based clustering, discriminant analysis and density estimation. J. Am. Stat. Assoc. 97(458), 611–631 (2002)
GeoJSON Format for Encoding Geographic Data Structures (2007). http://geojson.org/
Hough, S.E.: Earthquake intensity distribution: a new view. Bull. Earthq. Eng. 12(1), 135–155 (2014)
Johnson, S.C.: Hierarchical clustering schemes. J. Psychom. 32(3), 241–254 (1967)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)
Langfelder, P., Zhang, B., Horvath, S.: Defining clusters from a hierarchical cluster tree: the dynamic tree cut library for R. J. Bioinform. 24(5), 719–720 (2007)
Manning, C.D., Raghavan, P., Schutze, H.: Introduction to Information Retrieval. Cambridge University Press, Cambridge (2008)
Manning, C.D., Schutze, H.: Foundations of Statistical Natural Language Processing. MIT Press, Cambridge (2000)
Mclachlan, G.J., Peel, D.: Finite Mixture Models. Wiley, New York (2000)
Mclachlan, G.J., Basford, K.E.: Mixture Models: Inference and Applications to Clustering. Marcel Dekker, New York (1988)
Ngatchou-Wandji, J., Bulla, J.: On choosing a mixture model for clustering. J. Data Sci. 11(1), 157–179 (2013)
R Project for Statistical Computing (1997). http://www.r-project.org/
Rabiner, L.R.: A tutorial on hidden Markov models and selected applications in speech recognition. Proc. IEEE 77(2), 257–286 (1989)
Rajaraman, R., Ullman, J.D.: Mining of Massive Datasets. Cambridge University Press, New York (2011)
Salton, G., Wong, A., Yang, C.S.: A vector space model for automatic indexing. Commun. ACM 18(11), 613–620 (1975)
Schwarz, G.: Estimating the dimension of a model. Ann. Stat. 6(2), 461–464 (1978)
Theodoridis, Y.: SEISMO-SURFER: a prototype for collecting, querying, and mining seismic data. In: Manolopoulos, Y., Evripidou, S., Kakas, A.C. (eds.) PCI 2001. LNCS, vol. 2563, pp. 159–171. Springer, Heidelberg (2003)
United States Geological Survey (USGS) (2004). http://earthquake.usgs.gov/earthquakes/feed/v1.0/
Ward, J.H.: Hierarchical grouping to optimize an objective function. Am. Stat. Assoc. 58(301), 236–244 (1963)
Young, J.B., Presgrave, B.W., Aichele, H., Wiens, D.A., Flinn, E.A.: The Flinn-Engdahl regionalization scheme: the 1995 revision. Phys. Earth Planet. Inter. 96(4), 223–297 (1995)
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Bleiweiss, A. (2016). Reasoning Geo-Spatial Neutral Similarity from Seismic Data Using Mixture and State Clustering Models. In: Grueau, C., Gustavo Rocha, J. (eds) Geographical Information Systems Theory, Applications and Management. GISTAM 2015. Communications in Computer and Information Science, vol 582. Springer, Cham. https://doi.org/10.1007/978-3-319-29589-3_1
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