Detecting precursory patterns to enhance earthquake prediction in Chile

The prediction of earthquakes is a task of utmost difficulty that has been widely addressed by using many different strategies, with no particular good results thus far. Seismic time series of the four most active Chilean zones, the country with largest seismic activity, are analyzed in this study in order to discover precursory patterns for large earthquakes. First, raw data are transformed by removing aftershocks and foreshocks, since the goal is to only predict main shocks. New attributes, based on the well-known b-value, are also generated. Later, these data are labeled, and consequently discretized, by the application of a clustering algorithm, following the suggestions found in recent literature. Earthquakes with magnitude larger than 4.4 are identified in the time series. Finally, the sequence of labels acting as precursory patterns for such earthquakes are searched for within the datasets. Results verging on 70% on average are reported, leading to conclude that the methodology proposed is suitable to be applied in other zones with similar seismicity. Discovery of precursory patterns for medium-large earthquakes in Chile.b-value's ability to predict earthquakes is confirmed.General purpose methodology: can be applied to any other area.Accuracy rate above 70%.

[1]  Charles F. Richter,et al.  Earthquake magnitude, intensity, energy, and acceleration , 1942 .

[2]  Kristy F. Tiampo,et al.  Seismicity-based earthquake forecasting techniques: Ten years of progress , 2012 .

[3]  A. Frankel.  Mapping Seismic Hazard in the Central and Eastern United States , 1995 .

[4]  Vladimir Kossobokov,et al.  Earthquake Prediction, M8 Algorithm , 2011 .

[5]  ASHIF PANAKKAT,et al.  Neural Network Models for Earthquake Magnitude Prediction Using Multiple seismicity Indicators , 2007, Int. J. Neural Syst..

[6]  F. Martínez-Álvarez,et al.  Earthquake prediction in seismogenic areas of the Iberian Peninsula based on computational intelligence , 2013 .

[7]  J. B. Rundle,et al.  Systematic Procedural and Sensitivity Analysis of the Pattern Informatics Method for Forecasting Large (M > 5) Earthquake Events in Southern California , 2006 .

[8]  D. P. Schwartz,et al.  Fault behavior and characteristic earthquakes: Examples from the Wasatch and San Andreas Fault Zones , 1984 .

[9]  B. Gutenberg,et al.  Seismicity of the Earth , 1941 .

[10]  Kiehwa Lee,et al.  Historical Seismicity of Korea , 2006 .

[11]  Luz Cárdenas Jirón.  The chilean earthquake and tsunami 2010 , 2012 .

[12]  Martin S. Jaffe,et al.  Reducing earthquake risks : a planner's guide , 1981 .

[13]  Gerassimos A. Papadopoulos,et al.  OPERATIONAL EARTHQUAKE FORECASTING. State of Knowledge and Guidelines for Utilization , 2011 .

[14]  Rudolf Scitovski,et al.  A fast partitioning algorithm using adaptive Mahalanobis clustering with application to seismic zoning , 2014, Comput. Geosci..

[15]  Shinji Toda,et al.  Megacity Megaquakes—Two Near Misses , 2013, Science.

[16]  W. H. Bakun,et al.  Implications for prediction and hazard assessment from the 2004 Parkfield earthquake , 2005, Nature.

[17]  Arnaud Mignan,et al.  Retrospective on the Accelerating Seismic Release (ASR) hypothesis: Controversy and new horizons , 2011 .

[18]  Ragnar Stefánsson.  A new approach to earthquake prediction , 2011 .

[19]  Francisco Martínez-Álvarez,et al.  Determining the best set of seismicity indicators to predict earthquakes. Two case studies: Chile and the Iberian Peninsula , 2013, Knowl. Based Syst..

[20]  D. Agnew,et al.  Earthquakes: Future shock in California , 2005, Nature.

[21]  M. Wyss,et al.  Mapping spatial variability of the frequency-magnitude distribution of earthquakes , 2002 .

[22]  Xiang-Chu Yin,et al.  A new approach to earthquake prediction: The Load/Unload Response Ratio (LURR) theory , 1995 .

[23]  W. H. Bakun,et al.  The Parkfield, California, Earthquake Prediction Experiment , 1985, Science.

[24]  Francisco Martínez-Álvarez,et al.  Neural networks to predict earthquakes in Chile , 2013, Appl. Soft Comput..

[25]  Ali Akbar Safavi,et al.  Application of neural network and ANFIS model for earthquake occurrence in Iran , 2013, Earth Science Informatics.

[26]  G. A. Sobolev.  Precursory phases, seismicity precursors, and earthquake prediction in Kamchatka , 1999 .

[27]  O. Kulhánek,et al.  Spatial and temporal b value anomalies preceding the devastating off coast of NW Sumatra earthquake of December 26, 2004 , 2005 .

[28]  Yuki Sawai,et al.  Predecessors of the giant 1960 Chile earthquake , 2005, Nature.

[29]  L. A. Cárdenas-Jirón,et al.  The Chilean Earthquake and Tsunami 2010: A Multidisciplinary Study of Mw8.8, Maule , 2012 .

[30]  Jorge Reyes,et al.  A Chilean seismic regionalization through a Kohonen neural network , 2010, Neural Computing and Applications.

[31]  Antonino Laudani,et al.  Seismicity Pattern Changes before the M = 4.8 Aeolian Archipelago (Italy) Earthquake of August 16, 2010 , 2014, TheScientificWorldJournal.

[32]  Alicia Troncoso Lora,et al.  Computational Intelligence Techniques for Predicting Earthquakes , 2011, HAIS.

[33]  G. Ranalli.  A Statistical Study of Aftershock Sequences , 1969 .

[34]  B. Bolt,et al.  The standard error of the magnitude-frequency b value , 1982 .

[35]  T. Utsu.  A method for determining the value of b in a formula log n=a-bM showing the magnitude frequency relation for earthquakes , 1965 .

[36]  Alicia Troncoso Lora,et al.  Pattern recognition to forecast seismic time series , 2010, Expert Syst. Appl..