Abstract

To identify a set of earthquake precursors for predicting earthquakes in different tectonic environments, a series of geo-scientific tools and methodologies based on rigorous assessment of multi-parameters have been developed by different researchers without complete success in earthquake prediction. The aim of earthquake forecasting involve multi-components analysis in implementing probabilistic forecasts that resolves decision-making in a low-probability environment. The proposed work analytically examined some of the modern seismological earthquake algorithms used for analyzing seismo-electro-telluric-geodetic data used across the globe. The present study develops a fuzzy inference model by correlating evaluatory parameters by surveying analytical work of the data sets used,numerical experimentation done in analysis and the global application and success rate of 18 of the most viable earthquake prediction algorithms developed by mutually comparing different models in earthquake predictability experiments. Using qualitative analysis in probabilistic information, an efficient trust model has been implemented through fuzzy inferencing rules. Trust validity through information is an aggregation of consensus in earthquake occurrence given a set of past success rate and the methodologies involved in prediction.

References

• Andalib, A. , Zare, M. and Atry,F. , A fuzzy expert system for earthquake prediction, case study: the zagros range, ICMSAO, 2009. Crossref
• Asada, T. , ,Earthquake prediction techniques. – Their application, in Japan, University of Tokyo Press, pp- 317 ,1982. CrossRef
• Asaravala A. , Earth's Tides Set Off Quakes. Wired. com . http://www. wired. com /science/discoveries/ news/2004/ 10/65442
• Atzemoglou,A. , Kondopoulou, D. , Papamarinopoulos, S. ,Dimitriadis, S. , Paleomagnetic evidence for block rotations in the Western Greek Rhodope. Geophysical Journal International,118(1),221-230,doi:10. 1111/j. 1365-246X. 1994. tb04685. x,1994.
• Boschi, E. , Statistical Seismology: Physical and Stochastic Modeling of Earthquake Occurrence and Forecasting In: 28th workshop of the International School of Geophysics, 2007. Crossref
• Bufe C. G. and Varnes D. J. , Predicted modeling of the seismic cycle of the greater San Francisco Bay region, J. Geophys. Res. , 98, 9871- 9883, doi:10. 1029/93JB00357. ,1993.
• Chapelle O. , Chang Y. and Liu T. Y. , Future directions in learning to rank. Yahoo Learning to Rank Challenge JMLR: Workshop and Conference Proceedings 14 91-100, 2011. Crossref
• Di Giovambattista R. and Tyupkin Y. , Seismicity patterns before the M=5. 8 2002, Palermo (Italy) earthquake: seismic quiescence and accelerating seismicity. , Tectonophysics, 384, pp. 243-255,doi: 10. 1016/j. tecto. 2004. 04. 001,2004.
• Dmowska R. , Earthquake prediction--state of the art, Pageoph Topical Volumes
• Max Wyss Ed. , Birkhäuser Verlag, Basel ; Boston, USA, 1997. Crossref
• Dragoni, M. , and E. Boschi (ed. ), Earthquake prediction: proceedings of the International School of Solid Earth Geophysics, 5th course, Il Cigno Galileo Galilei edizioni di arte e scienza, Rome, 1992. Crossref
• P. Dutta, M. Naskar and O. Mishra, "Test of Strain Behavior Model with Radon Anomaly in Seismogenic Area: A Bayesian Melding Approach," International Journal of Geosciences, Vol. 3 No. 1, 2012, pp. 126-132.
• P. K. Dutta, M. K. Naskar and O. P. Mishra,Test of strain behavior model with Radon anomaly in earthquake prone zones. HIMALAYAN GEOLOGY (Journal):Vol 33 (1), 2012,pp-23-28.
• Ebel J. E. , Chambers D. W. , Kafka, A. L. and Baglivo J. A. , Non-Poissonian earthquake clustering and the hidden markov model as bases for earthquake forecasting in California, Seismological Research Letters; v. 78(1); p. 57-65; DOI: 10. 1785/gssrl. 78. 1. 57,2007.
• Field E. H. , Jordan T. H. and Cornell C. A. , Developing Community-Modeling Environment for Seismic Hazard Analysis. Seismological Research Letters; v. 74; no. 4; p. 406-419; DOI: 10. 1785/gssrl. 74. 4. 406, 2003.
• Gabrielov A. , Levshina T. A. and Rotwain I. M. , Block model of earthquake . Phys. Earth Planet. Int. , 61:18-28, doi:10. 1016/0031-9201(90)90091-B, 1990.
• Giacinto G. , Paolucci R. and Roli F. , "Application of neural networks and statistical pattern recognition algorithms to earthquake risk evaluation," Pattern Recognition Letters, vol. 18, pp. 1353–1362, doi>10. 1016/S0167-8655(97)00088-3,1997.
• Goetz S. J. , Fiske G. J. , Bunn A. J. , Using satellite time-series data sets to analyze fire disturbance and forest recovery across Canada, Remote Sensing of Environment 101 352–365, 2006. Crossref
• Guha S. K. and Patwardhan A. M. , Earthquake prediction : present status : proceedings of symposium held at the Department of Geology, University of Poona, Pune : University of Poona Pub. , Pune, India, 1985. Crossref
• Ishiguro, M. , A Bayesian approach to the analysis of the data of crustal movements. , J. Geod. Soc. Japan, 27, pp. 256-262, 1981.
• Jackson D. D. and Kagan Y. Y. , Testable earthquake forecasts for 1999, Seismol. Res. Lett. 70, 393–403, 1999. Crossref
• Jordan T. H. and THE RELM WORKING GROUP, Pure Appl. Geophys. 167, 859–876 DOI 10. 1007/s00024-010-0081-5, 2010.
• Keilis-Borok V. I. and Malinovskaya L. N. , One regularity in the occurrence of strong earthquakes. J. Geophys. Res. 69(14):3019–24, 1964. Crossref
• Keilis-Borok V. I. and Kossobokov V. G. , Premonitory activation of earthquake flow: algorithm M8. Phys. Earth Planet. Inter. 61:73–83, 1990. DOI: 10. 1146/annurev. ea. 19. 050191. 001403.
• Keilis-Borok V. I. and Kossobokov V. G. , Times of increased probability of strong earthquakes (M>=7. 5) diagnosed by algorithm M8 in Japan and adjacent territories. , J. Geophys. Res. , 95, B8, pp. 12413 – 12422, doi:10. 1029/JB095iB08p12413,1990a.
• Keilis-Borok V. I. and Rotwain I. M. , Diagnosis of time of increased probability of strong earthquakes in different regions of the world: algorithm CN. , Physics of the Earth and Planetary Interiors, 61, pp. 57-72, DOI: 10. 1016/0031-9201(90)90095-F,1990a.
• Keilis-Borok V. , Shebalin P. , Gabrielov A. and Turcotte D. , Reverse tracing of short-term earthquake precursors. , Physics of the earth and planetary interiors. , 145, pp. 75-85, DOI: 10. 1016/j. pepi. 2004. 02. 010, 2004.
• Kellis-Borok V. I. and Soloviev A. A. ,(eds. ), Nonlinear Dynamics of the Lithosphere and Earthquake Prediction(Springer-Verlag, Berlin-Heidelberg),337 p, 2003. Crossref
• Kisslinger C. , Practical Approaches to Earthquake Prediction and Warning. , Kluwer Academic Pub. , Amsterdam, Netherlands, 1986. Crossref
• Kossobokov V. G. ,Kellis-Borok V. I. and Smith S. W. ,Localization of intermediate-term earthquake prediction. J. Geophysics. Res. ,95(B12):19763-19772, 1990. Crossref
• Kolvankar V. G. , RF Emissions, Types Of Earthquake Precursors: Possibly Caused By The Planetary Alignments J. Ind. Geophys. Union Vol. 11, No. 3, pp. 147-160, 2007. Crossref
• Molchan G. M. , Strategies in strong earthquake prediction. Phys. Earth Planet. Inter. 61 (1–2),84–98, DOI: 10. 1016/0031-9201(90)90097-H,1990.
• Mohsin J. S. and Azam F. , Computational seismic algorithm comparison for earthquake prediction. International Journal of Geology. Volume 5(3), 2011. Crossref
• Morales- Esteban A. , Martinez-Alvarez F. , Troncoso A. , Justo J. L. and Rubio-Escudero C. , Pattern recognition to forecast seismic time series, , Experts System with Application 37 8333 – 8342, doi>10. 1016/j. eswa. 2010. 05. 050,2010.
• Mulargia F. and Gasperini P. , Evaluating the statistical validity beyond chance of VAN earthquake precursors, Geophys. J. Int. , 111, 33-44, 1992. Crossref[ps]
• Papazachos C. B. , Karakaisis G. F. , Savvaidis A. S. and Papazachos B. C. , Accelarating seismic crustal deformation in the Southern Aegean area. , Bull. Seism. Soc. Am. , 92, 570-580, DOI: 10. 1785/0120000223,2002
• Riedel K. S. ,Statistical Tests for Evaluating Earthquake Prediction Methods . Geophysical Research Letters,Vol. 23(11), PP. 1407-1409,doi:10. 1029/96GL00476, 1996.
• Rikitake T. and Yamazaki Y. , Small Earth strains as detected by electrical resistivity measurements. , Proc. Japan Acad. , Vol. 43, pp. 477 – 482,1967. Published in Surveys in Geophysics Volume 3( 2), 123-142, DOI: 10. 1007/BF01449189,1977.
• Rhoades D. A. and Evison F. F. , Long-range earthquake forecasting with every earthquake a precursor according to scale, Pure Appl. Geophys. 161(1), 47-72, DOI: 10. 1007/s00024-003-2434-9,2004.
• Rundle J. , Klein W. , Tiampo K. and Donnellan A. ,Strategies for the detection and analysis of space-time patterns of earthquakes on complex fault systems. In: Proceedings of the 2003 international conference on Computational science: PartIII. ISBN:3-540-40196-2,2003.
• Rundle J. B. , Turcotte D. L. and Klein W. , Geocomplexity and the Physics of Earthquakes, Geophysical MonographVol 120, doi: 10. 1029/GM120,American Geophysical Union, Washington, DC,2000.
• Rundle A. B. ,Aalsburg J. V. , Rundle P. , Turcotte D. and Morein G. ,Computing earthquake forecast probabilities using numerical simulations of the physics of realistic fault systems (Virtual California), Geophysical Research Abstracts,Vol. 10, doi:EGU2008-A-01955,2008.
• Shimazaki K. and Stuart W. , Earthquake prediction, Birkhäuser Pub. , Basel ; Boston, USA,1985. Crossref
• Shih-jung M. ,Introduction to earthquake prediction in China, Ti chen chiu pan she Pub. , Pei-ching, China,1993
• Sidorin Y. , Search for earthquake precursors in multidisciplinary data monitoring of geophysical and biological parameters Nat. Hazards Earth Syst. Sci. , 3, 153-158,2003. Crossref
• Sobolev G. A. , Tyupkin Y. S. and Zavialov A. , Map of Expectation Earthquakes Algorithm and RTL Prognostic Parameter: Joint Application. The 29th General Assembly of IASPEI, Thessaloniki, Greece, Abs:77,1997. Crossref
• Thanassoulas C. and Tselentis G. , Periodic variations in the earth's electric field as earthquake precursors: results from recent experiments in Greece. , Tectonophysics Volume: 224, Issue: 1-3, Pages: 103-111,1993. Crossref
• Varotsos P. , Eftaxias K. , Vallianatos F. and Lazaridou M. , Basic principles for evaluating an earthquake prediction method, Geophys. Res. Lett. , 23(11), 1295–1298, doi:10. 1029/96GL00905,1996.
• Vogel A. , Itsikara A. M. , Multidisciplinary approach to earthquake prediction: International Symposium on Earthquake Prediction in the North Anatolian Fault Zone (1980 : Istanbul, Turkey), proceedings of the International Symposium on Earthquake Prediction in the North Anatolian Fault Zone, held in Istanbul, March 31-April 5, 1980. , F. Vieweg Pub. , Braunschweig, Germany, 1982.
• Vogel A. , Terrestrial and Space techniques in earthquake prediction research. Proc. Int. Workshop on monitoring Crustal dynamics in earthquake zones, Strasbourgh, 29 Aug. – 5 Sept. 1978, Friedr. Vieweg and Sohn, Braunschweig, Germany, 1979.
• Vorobieva I. A. and Levshina T. A. , Prediction of a second large earthquake based on aftershock sequence. In: Computational Seismology and Geodynamics, 2:27–36. Washington, DC: Am. Geophys. Union, 1994.
• Walia V. ,Virk H. S. and Bajwa B. S. , Radon Precursory Signals for Some Earthquakes of Magnitude > 5 Occurred in N-W Himalaya: An Overview ,Pure and Applied Geophysics ,Vol 163(4), 711-721, DOI: 10. 1007/s00024-006-0044-z,2005.
• Wolfe C. J. ,On the Properties of Predominant-Period Estimators for Earthquake Early Warning Bulletin of the Seismological Society of America, Vol. 96(5), pp. 1961–1965, doi: 10. 1785/0120060017, 2006.
• Wyss M. , Slater L. and Burford R. O. , Decrease in deformation rate as a possible precursor to the next Parkfield earthquake, Nature 345, 428–431, doi:10. 1038/ 3454 28a0,1990b.
• Yin X. , Xuezhong C. , Ziping S. and Can Y. , A New Approach to Earthquake Prediction – The Load / Unload Response Ration (LURR) Theory, PAGEOPH, Vol. 145, No. 3 / 4, 701-715, DOI: 10. 1007/BF00879596, 1995.