Worldwide earthquake forecasts

  • Yan Y. KaganEmail author


We review global earthquake forecast as it was developed by our UCLA Group since 1970s. We discuss the new results on earthquake magnitude/moment distribution, especially the determination of maximum/corner moment magnitude. Recent successes of global geodetic surveys allow us to construct a high resolution map of the Earth surface displacement and, after converting this map to earthquake rate, combine these maps with those based on seismicity smoothing. Thus we are constructing and testing a global earthquake activity rate (GEAR) model as a function of the magnitude at 0.1 by 0.1\(^{\circ }\) resolution. Nicknamed “GEAR”, the model relies on a global strain rate model and the instrumental earthquake catalogs. Thus, for our forecast we use datasets that provide uniform global coverage: seismic catalogs, global plate boundary models, and global positioning system geodetic velocities. After testing, the model can be used by Global Earthquake Model (GEM) Foundation and others in seismic hazard estimation. GEAR covers magnitudes 5.8 and larger, the periods are from years to decades with no explicit time-dependence. The normalized magnitude distribution at each location is a combination of the tapered Gutenberg–Richter distributions with b-values and the corner magnitudes determined by a few global parameters that depend on the tectonic style and the focal mechanism properties. The seismic and strain-rate components of the model are specified separately and then combined optimally to fit an earthquake occurrence over the last several years. GEAR performs well in quasi-prospective tests using the global centroid–moment–tensor catalog after 2005 and the GEM catalog from 1918 to 1976; GEAR will be rigorously tested prospectively against future earthquakes. Because of its simplicity, GEAR can serve as a well-vetted reference model and as a null-hypothesis against which more complex models can be tested. With its high spatial resolution, GEAR can also be compared to detailed regional models. We also discuss potential methods to improve and extend the forecasts. Such methods include extending the forecast to lower magnitudes, introducing focal mechanisms into prediction methods, and making the short-term forecast an integral part of the practical forecast implementation.


Probability distributions Seismicity and tectonics Statistical seismology Dynamics: seismotectonics Subduction zones Maximum/corner magnitude 



I am grateful to David Jackson and Peter Bird of UCLA for useful discussions. The author appreciates partial support from the National Science Foundation through Grant EAR-1045876, as well as from the Southern California Earthquake Center (SCEC). SCEC is funded by NSF Cooperative Agreement EAR-0529922 and USGS Cooperative Agreement 07HQAG0008. The comments by an anonymous reviewer as well as by the Guest Editor (Alessandro Fasso) have improved the presentation. Publication 6260, SCEC.


  1. Aki K, Richards PG (2002) Quantitative seismology, 2nd edn. University Science Books, Sausalito, p 700Google Scholar
  2. Bateman H, Erdelyi A (1953) Higher transcendental functions. McGraw-Hill Co., New YorkGoogle Scholar
  3. Bird P (2003) An updated digital model of plate boundaries. Geochem Geophys Geosyst 4(3):1027. doi: 10.1029/2001GC000252 CrossRefGoogle Scholar
  4. Bird P (2009) Long-term fault slip rates, distributed deformation rates, and forecast of seismicity in the western United States from joint fitting of community geologic, geodetic, and stress direction data sets. J Geophys Res 114:B11403. doi: 10.1029/2009JB006317 CrossRefGoogle Scholar
  5. Bird P, Kagan YY (2004) Plate-tectonic analysis of shallow seismicity: apparent boundary width, beta, corner magnitude, coupled lithosphere thickness, and coupling in seven tectonic settings. Bull Seismol Soc Am 94(6):2380–2399 (plus electronic supplement)Google Scholar
  6. Bird P, Kreemer C (2015) Revised tectonic forecast of global shallow seismicity based on version 2.1 of the Global Strain Rate Map. Bull Seismol Soc Am 105(1):152–166. doi: 10.1785/0120140129 (plus electronic supplement)
  7. Bird P, Liu Z (2007) Seismic hazard inferred from tectonics: California. Seismol Res Lett 78(1):37–48CrossRefGoogle Scholar
  8. Bird P, Kagan YY, Jackson DD, Schoenberg FP, Werner MJ (2009) Linear and nonlinear relations between relative plate velocity and seismicity. Bull Seismol Soc Am 99(6):3097–3113 (plus electronic supplement)Google Scholar
  9. Bird P, Kreemer C, Holt WE (2010) A long-term forecast of shallow seismicity based on the Global Strain Rate Map. Seismol Res Lett 81(2):184–194 (plus electronic supplement)Google Scholar
  10. Bird P, Jackson DD, Kagan YY, Kreemer C, Stein RS (2015) GEAR1: a Global Earthquake Activity Rate model constructed from geodetic strain rates and smoothed seismicity. Bull Seismol Soc Am 105(5):2538–2554. doi: 10.1785/0120150058 (plus electronic supplement)
  11. Di Giacomo D, Bondar I, Storchak DA, Engdahl ER, Bormann P, Harris J (2015) ISC-GEM: Global Instrumental Earthquake Catalogue (1900–2009), III. Re-computed MS and mb, proxy MW, final magnitude composition and completeness assessment. Phys Earth Planet Inter 239(2):33–47. doi: 10.1016/j.pepi.2014.06.005 CrossRefGoogle Scholar
  12. Ekström G, Nettles M, Dziewonski AM (2012) The global CMT project 2004–2010: centroid-moment tensors for 13,017 earthquakes. Phys Earth Planet Inter 200–201:1–9. doi: 10.1016/j.pepi.2012.04.002 CrossRefGoogle Scholar
  13. Flinn EA, Engdahl ER, Hill AR (1974) Seismic and geographical regionalization. Bull Seismol Soc Am 64:771–992Google Scholar
  14. Fukutani Y, Suppasri A, Imamura F (2015) Stochastic analysis and uncertainty assessment of tsunami wave height using a random source parameter model that targets a Tohoku-type earthquake fault. Stoch Environ Res Risk Assess 29(7):1763–1779. doi: 10.1007/s00477-014-0966-4 CrossRefGoogle Scholar
  15. Geist EL, Parsons T (2016) Reconstruction of far-field tsunami amplitude distributions from earthquake sources. Geophys Pure Appl. doi: 10.1007/s00024-016-1288-x
  16. Goda K, Song J (2015) Uncertainty modeling and visualization for tsunami hazard and risk mapping: a case study for the 2011 Tohoku earthquake. Stoch Environ Res Risk Assess. doi: 10.1007/s00477-015-1146-x
  17. Hanks TC (1992) Small earthquakes, tectonic forces. Science 256:1430–1432CrossRefGoogle Scholar
  18. Helmstetter A, Kagan YY, Jackson DD (2006) Comparison of short-term and time-independent earthquake forecast models for southern California. Bull Seismol Soc Am 96(1):90–106CrossRefGoogle Scholar
  19. Jackson DD, Kagan YY (1999) Testable earthquake forecasts for 1999. Seismol Res Lett 70(4):393–403 (with electronic supplement)Google Scholar
  20. Kagan YY (1997) Seismic moment–frequency relation for shallow earthquakes: regional comparison. J Geophys Res 102(B2):2835–2852CrossRefGoogle Scholar
  21. Kagan YY (2003) Accuracy of modern global earthquake catalogs. Phys Earth Planet Inter 135(2–3):173–209CrossRefGoogle Scholar
  22. Kagan YY (2009) Testing long-term earthquake forecasts: likelihood methods and error diagrams. Geophys J Int 177(2):532–542CrossRefGoogle Scholar
  23. Kagan YY (2014) EARTHQUAKES: models, statistics, testable forecasts. Wiley, Hoboken. ISBN 978-1118637913Google Scholar
  24. Kagan YY, Jackson DD (1994) Long-term probabilistic forecasting of earthquakes. J Geophys Res 99(B7):13685–13700. doi: 10.1029/94JB00500 CrossRefGoogle Scholar
  25. Kagan YY, Jackson DD (2000) Probabilistic forecasting of earthquakes. Geophys J Int 143(2):438–453. doi: 10.1046/j.1365-246X.2000.01267.x CrossRefGoogle Scholar
  26. Kagan YY, Jackson DD (2011) Global earthquake forecasts. Geophys J Int 184(2):759–776. doi: 10.1111/j.1365-246X.2010.04857.x CrossRefGoogle Scholar
  27. Kagan YY, Jackson DD (2012) Whole Earth high-resolution earthquake forecasts. Geophys J Int 190(1):677–686. doi: 10.1111/j.1365-246X.2012.05521.x CrossRefGoogle Scholar
  28. Kagan YY, Jackson DD (2013) Tohoku earthquake: a surprise? Bull Seismol Soc Am 103(2B):1181–1194. doi: 10.1785/0120120110 CrossRefGoogle Scholar
  29. Kagan YY, Jackson DD (2014) Statistical earthquake focal mechanism forecasts. Geophys J Int 197(1):620–629. doi: 10.1093/gji/ggu015 CrossRefGoogle Scholar
  30. Kagan YY, Jackson DD (2015) Likelihood analysis of earthquake focal mechanism distributions. Geophys J Int 201(3):1409–1415. doi: 10.1093/gji/ggv085 CrossRefGoogle Scholar
  31. Kagan YY, Jackson DD (2016) Earthquake rate and magnitude distributions of great earthquakes for use in global forecasts. Geophys J Int 206(1):630–643. doi 10.1093/gji/ggw161 CrossRefGoogle Scholar
  32. Kagan Y, Knopoff L (1977) Earthquake risk prediction as a stochastic process. Phys Earth Planet Inter 14(2):97–108. doi: 10.1016/0031-9201(77)90147-9 CrossRefGoogle Scholar
  33. Kagan YY, Bird P, Jackson DD (2010) Earthquake patterns in diverse tectonic zones of the globe. Pure Appl Geophys 167(6/7):721–741. doi: 10.1007/s00024-010-0075-3 CrossRefGoogle Scholar
  34. Kreemer C, Blewitt G, Klein EC (2014) A geodetic plate motion and Global Strain Rate Model. Geochem Geophys Geosyst 15:3849–3889. doi: 10.1002/2014GC005407 CrossRefGoogle Scholar
  35. McCaffrey R (2008) Global frequency of magnitude 9 earthquakes. Geology 36(3):263–266. doi: 10.1130/G24402A.1 CrossRefGoogle Scholar
  36. Michael AJ (2014) How complete is the ISC-GEM global earthquake catalog? Bull Seismol Soc Am 104(4):1829–1837. doi: 10.1785/0120130227 CrossRefGoogle Scholar
  37. Romashkova LL (2009) Global-scale analysis of seismic activity prior to 2004 Sumatra–Andaman mega-earthquake. Tectonophysics 470:329–344. doi: 10.1016/j.tecto.2009.02.011 CrossRefGoogle Scholar
  38. Stein RS, Stirling MW (2015) Seismic hazard assessment: honing the debate, testing the models. Eos Trans AGU 96(13):12–15Google Scholar
  39. Storchak DA, Di Giacomo D, Engdahl ER, Harris J, Bondar I, Lee WHK, Bormann P, Villaseqor A (2015) The ISC-GEM global instrumental earthquake catalogue (1900–2009): introduction. Phys Earth Planet Inter 239:48–63. doi: 10.1016/j.pepi.2014.06.009 CrossRefGoogle Scholar
  40. Young JB, Presgrave BW, Aichele H, Wiens DA, Flinn EA (1996) The Flinn–Engdahl regionalisation scheme: the 1995 revision. Phys Earth Planet Inter 96:223–297CrossRefGoogle Scholar
  41. Zechar JD, Schorlemmer D, Liukis M, Yu J, Euchner F, Maechling PJ, Jordan TH (2010) The Collaboratory for the Study of Earthquake Predictability perspective on computational earthquake science. Concurr Comput Pract Exp 22(12):1836–1847. doi: 10.1002/cpe.1519 CrossRefGoogle Scholar
  42. Zechar JD, Schorlemmer D, Werner MJ, Gerstenberger MC, Rhoades DA, Jordan TH (2013) Regional earthquake likelihood models I: first-order results. Bull Seismol Soc Am 103(2a):787–798. doi: 10.1785/0120120186 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg (outside the USA) 2016

Authors and Affiliations

  1. 1.Department of Earth, Planetary, and Space SciencesUniversity of CaliforniaLos AngelesUSA

Personalised recommendations