Pattern Informatics and the Soup-of-Groups Model of Earthquakes: A Case Study of Italian Seismicity
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Pure and Applied Geophysics
Pattern Informatics and the Soup-of-Groups Model of Earthquakes: A Case Study of Italian Seismicity LING-YUN CHANG,1 CHIEN-CHIH CHEN,1,2 LUCIANO TELESCA,3 HSIEN-CHI LI,1 and S. A. CHEONG4 Abstract—An improved pattern informatics (PI) method, inspired by the soup-of-groups model, is developed in this study. This improved method reveals its potential in reducing the noise that, when the classical PI is applied, can yield misleading conclusions about the existence of ‘‘hotspots’’ in the vicinity of the epicenter of a strong earthquake. The application of this new method to Italian seismicity from 1985 to 2018 properly reveals ‘‘hotspots’’ around the two strongest earthquakes that occurred in 2009 (L’Aquila earthquake) and 2016 (Norcia earthquake), suggesting its utility in studies devoted to earthquake prediction. Keywords: Pattern informatics, soup-of-groups model, seismicity, Italy.
1. Introduction Widely employed investigative methods for midterm earthquake prediction include stress analysis and seismicity analysis. Ma et al. (2005) used stress analysis to examine static stress in Taiwan during the seismogenic process of the Chi–Chi earthquake. Lambert et al. (2009) discussed the relationship between calculated theoretical tidal stresses and tremors in the northern Cascadia subduction zone. Seismicity-based earthquake forecasting/prediction
1 Department of Earth Sciences and Institute of Geophysics, National Central University, Jhongli, Taoyuan 32001, Taiwan, ROC. 2 Earthquake-Disaster and Risk Evaluation and Management (E-DREaM) Center, National Central University, Jhongli, Taoyuan 32001, Taiwan, ROC. 3 National Research Council, Institute of Methodologies for Environmental Analysis, 85050 Tito, PZ, Italy. E-mail: [email protected] 4 Division of Physics and Applied Physics, School of Physical and Mathematical Sciences, Nanyang Technological University, 21 Nanyang Link, Singapore 637371, Republic of Singapore.
techniques have progressed rapidly in the last two decades. Seismicity analysis approaches include the region-time-length algorithm (Chen and Wu 2005), the Z test (Wu et al. 2008a, b), b-value analysis (Wu et al. 2008a, b) and the pattern informatics (PI) algorithm (Rundle et al. 2000, 2003; Tiampo et al. 2002; Chen et al. 2005, 2006). Tiampo and Shcherbakov (2012) published a comprehensive review article on seismicity-based earthquake prediction/forecasting techniques. Rundle et al. proposed the pattern informatics (PI) method to calculate seismicity rate changes before large earthquakes (Rundle et al. 2000, 2003; Tiampo et al. 2002; Chen et al. 2005, 2006). They divided the study area into small grids and then calculated the seismicity rates in two time intervals of (t0, t1) and (t0, t2) at each grid. The difference between the two seismicity rates was then calculated as the relative PI index. Spatial grids with high PI values are defined as anomaly areas, the so-called PI hotspots, and are presumed to have relative high probability of earthquake occurrences. Seismic
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