Development of a Combination Approach for Seismic Hazard Evaluation

AbstractWe developed a synth esis approach to augment current techniques for seismic hazard evaluation by combining four previously unrelated subjects: the pattern informatics (PI), load/unload response ratio (LURR), state vector (SV), and accelerating moment release (AMR) methods. Since the PI is proposed in the premise that the change in the seismicity rate is a proxy for the change in the tectonic stress, this method is used to quantify localized changes surrounding the epicenters of large earthquakes to objectively quantify the anomalous areas (hot spots) of the upcoming events. On the short-to-intermediate-term estimation, we apply the LURR, SV, and AMR methods to examine the hazard regions derived from the PI hot spots. A predictive study of the 2014 earthquake tendency in Chinese mainland, using the seismic data from 1970-01-01 to 2014-10-01, shows that, during Jan 01 to Oct 31, 2014, most of the M > 5.0 earthquakes, especially the Feb 12 M7.3 Yutian, May 30 M6.1 Yingjiang, Aug. 03 M6.5 Ludian, and Oct 07 M6.6 earthquakes, occurred in the seismic hazard regions predicted. Comparing the predictions produced by the PI and combination approaches, it is clear that, by using the combination approach, we can screen out the false-alarm regions from the PI estimation, without reducing the hit rate, and therefore effectively augment the predictive power of current techniques. This provided evidence that the multi-method combination approach may be a useful tool to detect precursory information of future large earthquakes.

[1]  David J. Varnes,et al.  Predictive modeling of the seismic cycle of the Greater San Francisco Bay Region , 1993 .

[2]  Ruth A. Harris,et al.  Introduction to Special Section: Stress Triggers, Stress Shadows, and Implications for Seismic Hazard , 1998 .

[3]  D. Turcotte,et al.  Micro and macroscopic models of rock fracture , 2003 .

[4]  Rongjiang Wang,et al.  Impact of the receiver fault distribution on aftershock activity , 2010 .

[5]  J. Douglas Zechar,et al.  Bayesian Forecast Evaluation and Ensemble Earthquake Forecasting , 2012 .

[6]  J. B. Rundle,et al.  Self-organization in leaky threshold systems: The influence of near-mean field dynamics and its implications for earthquakes, neurobiology, and forecasting , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[7]  Zheng‐Kang Shen,et al.  Increasing critical sensitivity of the Load/Unload Response Ratio before large earthquakes with identified stress accumulation pattern , 2006 .

[8]  Michael Brereton,et al.  A Modern Course in Statistical Physics , 1981 .

[9]  T. Jordan.  Earthquake Predictability, Brick by Brick , 2006 .

[10]  Xiang-Chu Yin,et al.  Development of a New Approach to Earthquake Prediction: Load/Unload Response Ratio (LURR) Theory , 2000 .

[11]  Y. Ben-Zion,et al.  Accelerated Seismic Release and Related Aspects of Seismicity Patterns on Earthquake Faults , 2002 .

[12]  Huai-Zhong Yu,et al.  A probabilistic approach for earthquake potential evaluation based on the load/unload response ratio method , 2010, Concurr. Comput. Pract. Exp..

[13]  Kristy F. Tiampo,et al.  Linear pattern dynamics in nonlinear threshold systems , 2000 .

[14]  J. Douglas Zechar,et al.  The Area Skill Score Statistic for Evaluating Earthquake Predictability Experiments , 2010 .

[15]  V. Keilis-Borok.  A worldwide test of three long-term premonitory seismicity patterns—a review , 1982 .

[16]  Jin Ma,et al.  Active tectonic blocks and strong earthquakes in the continent of China , 2003, Science in China Series D Earth Sciences.

[17]  Jia Cheng,et al.  Multi-Methods Combined Analysis of Future Earthquake Potential , 2011, Pure and Applied Geophysics.

[18]  Huaizhong Yu,et al.  State Vector: A New Approach to Prediction of the Failure of Brittle Heterogeneous Media and Large Earthquakes , 2006 .

[19]  David A. Rhoades,et al.  Mixture Models for Improved Short-Term Earthquake Forecasting , 2009 .

[20]  L. R. Sykes,et al.  Evolving Towards a Critical Point: A Review of Accelerating Seismic Moment/Energy Release Prior to Large and Great Earthquakes , 1999 .

[21]  Tamaz Chelidze,et al.  Maps of expected earthquakes based on a combination of parameters , 1991 .

[22]  Egill Hauksson,et al.  State of stress from focal mechanisms before and after the 1992 landers earthquake sequence , 1994, Bulletin of the Seismological Society of America.

[23]  G. Molchan,et al.  Structure of optimal strategies in earthquake prediction , 1991 .

[24]  Lang-Ping Zhang,et al.  Comparison Between LURR and State Vector Analysis Before Strong Earthquakes in Southern California Since 1980 , 2008 .

[25]  Xiang-Chu Yin,et al.  Spatial and Temporal Variation of LURR and its Implication for the Tendency of Earthquake Occurrence in Southern California , 2004 .

[26]  J. D. Zechar,et al.  Combining earthquake forecasts using differential probability gains , 2014, Earth, Planets and Space.

[27]  E. Hauksson,et al.  The 1999 Mw 7.1 Hector Mine, California, Earthquake Sequence: Complex Conjugate Strike-Slip Faulting , 2002 .

[28]  Xiang-Chu Yin,et al.  Acoustic Emission Experiments of Rock Failure Under Load Simulating the Hypocenter Condition , 2006 .

[29]  W. Klein,et al.  Pattern Dynamics and Forecast Methods in Seismically Active Regions , 2002 .

[30]  J. Carlson,et al.  Active zone size versus activity: A study of different seismicity patterns in the context of the prediction algorithm M8 , 1995 .

[31]  E. Hauksson,et al.  Crustal stress field in southern California and its implications for fault mechanics , 2001 .

[32]  Y. Ben‐Zion,et al.  Distributed damage, faulting, and friction , 1997 .

[33]  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 .

[34]  J. D. Zechar,et al.  Likelihood-Based Tests for Evaluating Space–Rate–Magnitude Earthquake Forecasts , 2009 .

[35]  Dion Weatherley,et al.  Load-Unload Response Ratio and Accelerating Moment/Energy Release Critical Region Scaling and Earthquake Prediction , 2002 .

[36]  Frank Press,et al.  Pattern recognition applied to earthquake epicenters in California , 1976 .

[37]  Matthias Holschneider,et al.  From Alarm‐Based to Rate‐Based Earthquake Forecast Models , 2012 .

[38]  D. Turcotte,et al.  Precursory Seismic Activation and Critical-point Phenomena , 2000 .

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

[40]  H. Kanamori.  The energy release in great earthquakes , 1977 .

[41]  Didier Sornette.  Mean-field solution of a block-spring model of earthquakes , 1992 .

[42]  John B. Rundle,et al.  A physical model for earthquakes: 3. Thermodynamical approach and its relation to nonclassical theories of nucleation , 1989 .

[43]  John B. Rundle,et al.  Statistical physics approach to understanding the multiscale dynamics of earthquake fault systems , 2003 .

[44]  Geoffrey C. P. King,et al.  Accelerating seismicity and stress accumulation before large earthquakes , 2001 .

[45]  L. Reichl.  A modern course in statistical physics , 1980 .

[46]  D. Turcotte,et al.  Reverse tracing of short-term earthquake precursors , 2004 .