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Natural Hazards

, Volume 69, Issue 2, pp 1155–1177 | Cite as

Earthquake prediction: 20 years of global experiment

  • Vladimir G. Kossobokov
Original Paper

Abstract

Earthquake professionals have for many decades recognized the benefits to society from reliable earthquake predictions, but uncertainties regarding source initiation, rupture phenomena, and accuracy of both the timing and magnitude of the earthquake occurrence have oftentimes seemed either very difficult or impossible to overcome. The problem is that most of these methods cannot be adequately tested and evaluated either because of (a) lack of a precise definition of “prediction” and/or (b) shortage of data for meaningful statistical verification. This is not the case for the pattern recognition algorithm M8 designed in 1984 for prediction of great, Magnitude 8, earthquakes, hence its name. By 1986, the algorithm was rescaled for applications aimed at smaller magnitude earthquakes, down to M5+ range, and since then it has become a useful tool for systematic monitoring of seismic activity in a number of test seismic regions worldwide. After confirmed predictions of both the 1988 Spitak (Armenia) and the 1989 Loma Prieta (California) earthquakes, a “rigid test” to evaluate the efficiency of the intermediate-term middle-range earthquake prediction technique has been designed. Since 1991, each half-year, the algorithm M8 alone and in combination with its refinement MSc has been applied in a real-time prediction mode to seismicity of the entire Earth, and this test outlines, where possible, the areas in the two approximations where magnitude 8.0+ and 7.5+ earthquakes are most likely to occur before the next update. The results of this truly global 20-year-old experiment are indirect confirmations of the existing common features of both the predictability and the diverse behavior of the Earth’s naturally fractal lithosphere. The statistics achieved to date prove (with confidence above 99 %) rather high efficiency of the M8 and M8-MSc predictions limited to intermediate-term middle- and narrow-range accuracy. These statistics support the following general conclusions—(1) precursory seismic patterns do exist; (2) the size of an area where precursory seismic patterns show up is much larger than that of the source zone of the incipient target earthquake; (3) many precursory seismic patterns appear to be similar, even in regions of fundamentally different tectonic environments; and (4) some precursory seismic patterns are analogous to those in advance of extreme catastrophic events in other complex nonlinear systems (e.g., magnetic storms, solar flares, “starquakes”, etc.)—that are of high importance for further searches of the improved earthquake forecast/prediction algorithms and methods.

Keywords

Extreme events Statistics Forecast Prediction Earthquakes Precursory patterns 

Notes

Acknowledgments

The author is indebted to Vladimir I. Keilis-Borok, John H. Healy, James W. Dewey, and Stewart W. Smith for invaluable discussions and comments at the design of the earthquake prediction algorithms and their global testing. Special thanks to Dr. James L. Bela for suggestions that have helped improving the text.

References

  1. Allen CR (Chair), Edwards W, Hall WJ, Knopoff L, Raleigh CB, Savit CH, Toksoz MN, Turner RH (1976) Predicting earthquakes: A scientific and technical evaluation—with implications for society. Panel on Earthquake Prediction of the Committee on Seismology, Assembly of Mathematical and Physical Sciences, National Research Council, US National Academy of Sciences, Washington, DCGoogle Scholar
  2. Antonyan AS, Manukyan AV, Romashkova LL, Kossobokov VG (2007) Re-establishing seismic monitoring aimed at intermediate-term prediction of strong earthquakes in Armenia. Geophys Res Abstr 9:EGU2007-A-06626 (Abstracts of the Contributions of the EGU General Assembly 2007, Vienna, Austria, 15–20 April 2007, CD-ROM)Google Scholar
  3. Dobrovolsky IR, Zubkov SI, Myachkin VI (1979) Estimation of the size of earthquake preparation zone. Pure Appl Geophys 117:1025–1044CrossRefGoogle Scholar
  4. Gabrielov A, Newman WI, Turcotte DL (1999) An exactly soluble hierarchical clustering model: inverse cascades, self-similarity, and scaling. Phys Rev E 60:5293–5300CrossRefGoogle Scholar
  5. Gelfand IM (1991) Two archetypes in the psychology of Man. Nonlinear Sci Today 1(4):11Google Scholar
  6. Gelfand I, Guberman Sh, Keilis-Borok V, Knopoff L, Press F, Ransman E, Rotwain I, Sadovsky A (1976) Pattern recognition applied to earthquakes epicenters in California. Phys Earth Planet Inter 11:227–283CrossRefGoogle Scholar
  7. Giardini D, Grünthal G, Shedlock KM, Zhang P (1999) The GSHAP global seismic hazard map. Annali di Geofis 42(6):1225–1228Google Scholar
  8. Gorshkov A, Kossobokov V, Soloviev A (2003) Recognition of Earthquake-prone areas. In: Keilis-Borok VI, Soloviev AA (eds) Nonlinear dynamics of the lithosphere and earthquake prediction. Springer, Heidelberg, pp 239–310CrossRefGoogle Scholar
  9. Harte D, Li D-F, Vreede M, Vere-Jones D (2003) Quantifying the M8 prediction algorithm: reduction to a single critical variable and stability results. New Zealand J Geol Geophys 46:141–152CrossRefGoogle Scholar
  10. Healy JH, Kossobokov VG, Dewey JW (1992) A test to evaluate the earthquake prediction algorithm, M8, US Geol. Surv. Open-File Report 92-401, 23 p. with 6 AppendicesGoogle Scholar
  11. Kanamori H (1981) The nature of seismicity patterns before large earthquakes. In: Ewing M (ed) Series 4: Earthquake prediction—an international review. AGU Geophys Mono, Washington, DC, pp 1–19Google Scholar
  12. Kanamori H (2005) Real-time seismology and earthquake damage mitigation. Annu Rev Earth Planet Sci 33:195–214CrossRefGoogle Scholar
  13. Keilis-Borok VI (1990) The lithosphere of the Earth as a nonlinear system with implications for earthquake prediction. Rev Geophys 28:19–34CrossRefGoogle Scholar
  14. Keilis-Borok VI, Kossobokov VG (1984) A complex of long-term precursors for the strongest earthquakes. In: Earthquakes and preventive measures for natural catastrophes. The 27th international geological congress, Moscow, USSR, August 4–14, 1984, Colloquium 06 (in Russian), vol. 61, Nauka, Moscow, pp 56–66Google Scholar
  15. Keilis-Borok VI, Kossobokov VG (1987) Periods of high probability of occurrence of the world’s strongest earthquakes. Computational Seismol 19:45–53, Allerton Press Inc.Google Scholar
  16. Keilis-Borok VI and Kossobokov VG (1988) Premonitory activation of seismic flow: Algorithm M8. Lecture Notes of the Workshop on Global Geophysical Informatics with Applications to Research in Earthquake Prediction and Reduction of Seismic Risk (15 Nov.–16 Dec., 1988), ICTP, Trieste, 17 pGoogle Scholar
  17. Keilis-Borok VI, Kossobokov VG (1990) Premonitory activation of seismic flow: algorithm M8. Phys Earth Planet Inter 61:73–83CrossRefGoogle Scholar
  18. Keilis-Borok VI, Malinovskaya LN (1964) One regularity in the occurrence of strong earthquakes. J Geophys Res 69:3019–3024CrossRefGoogle Scholar
  19. Keilis-Borok VI, Knopoff L, Kossobokov V, Rotwain IM (1990) Intermediate-term prediction in advance of the Loma Prieta earthquake. Geophys Res Lett 17(9):1461–1464CrossRefGoogle Scholar
  20. Keilis-Borok V, Kossobokov V, Healy J, Turcotte D (2000) Reproducible earthquake prediction and earthquake preparedness. In: Geophys Res Abstr (European Geophysical Society, 25th General Assembly, CD-ROM)Google Scholar
  21. Keilis-Borok V, Shebalin P, Gabrielov A, Turcotte D (2004) Reverse tracing of short-term earthquake precursors. Phys Earth Planet Inter 145(1–4):75–85CrossRefGoogle Scholar
  22. Kossobokov VG (1986) The test of algorithm M8. In: Sadovsky MA (ed) Algorithms of long-term earthquake prediction. CERESIS, Lima, Peru, pp 42–52Google Scholar
  23. Kossobokov VG (1997) User manual for M8. In: Healy JH, Keilis-Borok VI, Lee WHK (eds) Algorithms for earthquake statistics and prediction. IASPEI Software Library, vol 6. Seismol Soc Am, El Cerrito, CAGoogle Scholar
  24. Kossobokov V (2005) Can we predict mega-earthquakes. Geophys Res Abstr 7: 05832. Abstracts of the Contributions of the EGU General Assembly 2005, Vienna, Austria, 24–29 April, 2005 (CD-ROM)Google Scholar
  25. Kossobokov V (2008) Testing earthquake forecast/prediction methods: “Real-time forecasts of tomorrow’s earthquakes in California”. Geophys Res Abstr 10:EGU2008-A-07826. Abstracts of the Contributions of the EGU General Assembly 2008, Vienna, Austria, 13-18 April 2008 (CD-ROM)Google Scholar
  26. Kossobokov V (2011) Are mega earthquakes predictable? Izv Atmos Ocean Phys 46(8):951–961. doi: 10.1134/S0001433811080032 CrossRefGoogle Scholar
  27. Kossobokov VG, Nekrasova AK (2010) Global seismic hazard assessment program maps are misleading. Eos Trans AGU 91(52):U13A-0020, Fall Meet. SupplGoogle Scholar
  28. Kossobokov VG, Nekrasova AK (2011) Global seismic hazard assessment program (GSHAP) maps are misleading. Probl Eng Seismol 38(1):65–76 (in Russian)Google Scholar
  29. Kossobokov VG, Soloviev AA (2008) Prediction of extreme events: Fundamentals and prerequisites of verification. Russ J Earth Sci 10:ES2005. doi: 10.2205/2007ES000251
  30. Kossobokov VG, Keilis-Borok VI, Smith SW (1990) Localization of intermediate-term earthquake prediction. J Geophys Res 95(12):19763–19772CrossRefGoogle Scholar
  31. Kossobokov VG, Healy JH, Dewey JW (1997) Testing an earthquake prediction algorithm. Pure Appl Geophys 149:219–232CrossRefGoogle Scholar
  32. Kossobokov VG, Maeda K, Uyeda S (1999a) Precursory activation of seismicity in advance of the Kobe, 1995 earthquake. Pure Appl Geophys 155:409–423CrossRefGoogle Scholar
  33. Kossobokov VG, Romashkova LL, Keilis-Borok VI, Healy JH (1999b) Testing earthquake prediction algorithms: Statistically significant real-time prediction of the largest earthquakes in the Circum-Pacific, 1992–1997. Phys Earth Planet Inter 111(3–4):187–196CrossRefGoogle Scholar
  34. Kossobokov VG, Shebalin PN, Healy JH, Dewey JW, Tikhonov IN (1999c) A real-time intermediate-term prediction of the October 4, 1994, and December 3, 1995, southern Kuril Islands earthquakes. In: Chowdhury DK (ed) Computational Seismol Geodyn/Am Geophys Un 4. The Union, Washington, DC, pp 57–63Google Scholar
  35. Kossobokov VG, Keilis-Borok VI, Cheng B (2000) Similarities of multiple fracturing on a neutron star and on the Earth. Phys Rev E 61:3529–3533CrossRefGoogle Scholar
  36. Kossobokov VG, Romashkova LL, Panza GF, Peresan A (2002) Stabilizing intermediate-term medium-range earthquake predictions. J Seismol Earthq Eng 4(2–3):11–19Google Scholar
  37. Kossobokov VG, Romashkova LL, Nekrasova AK (2008) Targeting the next mega-earthquake. Geophys Res Abstr 10:EGU2008-A-07303. Abstracts of the contributions of the EGU general assembly 2008, Vienna, Austria, 13–18 April 2008 (CD-ROM)Google Scholar
  38. Kossobokov VG, Romashkova LL, Nekrasova AK (2010) Targeting the next mega-earthquake: the 27 February 2010 Chile case. Eos Trans AGU 91(26):U41A-06, Meet Am SupplGoogle Scholar
  39. Molchan GM (1994) Models for optimization of earthquake prediction. In: Chowdhury DK (ed) Computational Seismol Geodyn 1:1–10. Am Geophys Un. The Union, Washington, DCGoogle Scholar
  40. Molchan GM (2003) Earthquake prediction strategies: a theoretical analysis. In: Keilis-Borok VI, Soloviev AA (eds) Nonlinear dynamics of the lithosphere and earthquake prediction. Springer, Heidelberg, pp 209–237CrossRefGoogle Scholar
  41. Panza GF, Irikura K, Kouteva-Guentcheva M, Peresan A, Wang Z, Saragoni R (eds) (2011) Advanced seismic hazard assessment. Pure Appl Geophys 168, Springer, Basel, 1st edn, 752 pGoogle Scholar
  42. Peresan A, Kossobokov V, Romashkova L, Panza GF (2005) Intermediate-term middle-range earthquake predictions in Italy: a review. Earth Sci Rev 69(1–2):97–132CrossRefGoogle Scholar
  43. Popa M, Cadichian N, Romashkova LL, Radulian M, Stanica D, Kossobokov VG (2007) Seismic monitoring aimed at intermediate-term prediction of strong earthquakes in the Vrancea region. Geophys Res Abstr 9: EGU2007-A-06563. Abstracts of the contributions of the EGU general assembly 2007, Vienna, Austria, 15–20 April 2007 (CD-ROM)Google Scholar
  44. Richter CF (1964) Discussion of paper by V.I. Keylis-Borok and L.N. Malinovskaya, ‘One regularity in the occurrence of strong earthquakes’. J Geophys Res 69:3025CrossRefGoogle Scholar
  45. Romashkova L, Nekrasova A, Kossobokov V (2005) Seismic cascades in advance and after 26 December 2004 Sumatra-Andaman and other mega-earthquakes. Eos Trans AGU 86(52):U11B-0834, Fall Meet. SupplGoogle Scholar
  46. Scholz CH (1997) Whatever happened to earthquake prediction. Geotimes 42(3):16–19Google Scholar
  47. Shaw JH, Shearer PM (1999) An elusive blind-thrust fault beneath metropolitan Los Angeles. Science 238:1516–1518CrossRefGoogle Scholar
  48. Shebalin P (1992) Automatic duplicate identification in set of earthquake catalogues merged together. US Geol Surv Open File Rep 92-401, Appendix IIGoogle Scholar
  49. Shebalin P (2006) Increased correlation range of seismicity before large events manifested by earthquake chains. Tectonophys 424(3–4):335–349CrossRefGoogle Scholar
  50. Updike RG (Compiler) (1989) Proceedings of the National Earthquake Prediction Evaluation Council, June 6–7, 1988, Reston, VA, US Geol Surv Open-File Rep 89–114Google Scholar
  51. Wyss M, Nekrasova A, Kossobokov VG (2011) Errors in expected human losses due to incorrect seismic hazard estimates. Eos Trans AGU 92(52):NH12A-04. Fall Meet SupplGoogle Scholar
  52. Wyss M, Nekrasova A, Kossobokov VG (2012) Errors in expected human losses due to incorrect seismic hazard estimates. Accepted for publication in Natural HazardsGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2012

Authors and Affiliations

  1. 1.Russian Academy of SciencesInstitute of Earthquake Prediction Theory and Mathematical GeophysicsMoscowRussian Federation
  2. 2.Institut de Physique du Globe de ParisParisFrance

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