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Long-term earthquake forecasting model for northeast India and surrounding region: seismicity-based model

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Abstract

An earthquake forecasting model has been proposed for northeast India and surrounding region in a specified time span by exploiting the temporal and the spatial variations in the Gutenberg and Richter parameters (a and b). The study region has been divided into grid with a grid interval of 0.1° in latitude and longitude and 14 zones for the seismic activity density and the seismicity parameters estimation, respectively, while the time span of the used earthquake catalog has been divided into time grids with a fixed time interval of 5 years. The seismic activity rate at a grid point has been estimated from seismic activity density at the grid point and seismic activity in zone comprising the grid. The normalized multivariate Gaussian mixture model is considered to estimate the seismic activity density, while seismicity parameters have been estimated using the Gutenberg and Richter relation and the method of maximum likelihood estimation. The seismic activity rates at each grid point in all time grids have been fitted to an autoregressive model of order three, and the inferred coefficients of the model has been used to forecast seismic activity rate in next time grid, e.g., in time grid from 2011 to 2015 including.

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References

  • Aki K (1965) Maximum likelihood estimate of b in the formula log N = a − bM and its confidence limits. Bull Earthq Res Inst 43:237–239

    Google Scholar 

  • Bachmann CE, Wiemer S, Goertz-Allmann BP, Woessner J (2012) Influence of pore-pressure on the event-size distribution of induced earthquakes. Geophys Res Lett. doi:10.1029/2012GL051480

    Google Scholar 

  • Bilham R (2004) Earthquakes in India and the Himalaya: tectonics, geodesy and history. Ann Geophys 47(2):839–858

    Google Scholar 

  • Cao L, Fang H, Li Q, Chen J (1996) Forecasting b-values for seismic events. Int J Bifurc Chaos 6:545–555

    Article  Google Scholar 

  • Chatfield C (2004) The analysis of time series. Chapman and Hall, Florida

    Google Scholar 

  • Das S, Gupta ID, Gupta VK (2006) A probabilistic seismic hazard analysis of northeast India. Earthq Spectra 22(1):1–27

    Article  Google Scholar 

  • Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via EM algorithm. J R Stat Soc B Stat Methodol 1:1–38

    Google Scholar 

  • Efron B (1979) Bootstrap methods: another look at jackknife. Ann Stat 7:1–26

    Article  Google Scholar 

  • Everitt BS (1993) Cluster analysis. Edward Arnold, London

    Google Scholar 

  • Fawcett T (2006) An introduction to ROC analysis, pattern recognition. Letter 27:861–874

    Google Scholar 

  • Frohlich C, Davis S (1993) Teleseismic b-values: or, much ado about 1.0. J Geophys Res 98:631–644

    Article  Google Scholar 

  • Gerstenberger M, Wiemer S, Giardini D (2001) A systematic test of the hypothesis that the b value varies with depth in California. Geophys Res Lett 28:57–60

    Article  Google Scholar 

  • Guo Z, Ogata Y (1997) Statistical relations between the parameters of aftershocks in time, space and magnitude. J Geophys Res 102:2857–2873

    Article  Google Scholar 

  • Gutenberg B, Richter CF (1944) Frequency of earthquakes in California. Bull Seismol Soc Am 34:185–188

    Google Scholar 

  • Hainzl S (2003) Self-organization of earthquake swarms. J Geodyn 35:157–172

    Article  Google Scholar 

  • Hamilton JD (1994) Time series analysis. Princeton University Press, Princeton

    Google Scholar 

  • Hettmansperger TP, Thomas H (2000) Almost nonparametric inference for repeated measure in mixture models. J R Stat Soc B 62:811–825

    Article  Google Scholar 

  • Imoto M (1991) Changes in the magnitude frequency b-value prior to large M ≥ 6.0 earthquakes in Japan. Tectonophysics 193:311–325

    Article  Google Scholar 

  • Imoto M, Hurukawa N, Ogata Y (1990) Three-dimensional spatial variations of b value in the Kanto area, Japan. Zishin 43:321–326

    Google Scholar 

  • Kagan YY, Jackson DD (2000) Probabilistic forecasting of earthquakes. Geophys J Int 143:438–453

    Article  Google Scholar 

  • Kolathayar S, Sitharam TG (2012) Characterization of regional seismic source zones in and around India. Seismol Res Lett 83(1):77–85. doi:10.1785/gssrl.83.1.77v

    Article  Google Scholar 

  • Kramer SL (1996) Geotechnical earthquake engineering. Prentice Hall, Upper Saddle River

    Google Scholar 

  • MacDonald PDM (1975) Estimation of finite distribution mixtures. In: Gupta RP (ed) Applied statistics. North-Holland, Amsterdam, pp 231–245

    Google Scholar 

  • McLachlan G, Ng S (2009) The EM algorithm. In: Wu X, Kumar V (eds) The top-ten algorithms in data mining, CRC, Boca Raton, pp 93–115

  • Murru M, Console R, Falcone G (2009) Real time earthquake forecasting in Italy. Tectonophysics 470:214–223

    Article  Google Scholar 

  • Nath SK, Thingbaijam KKS (2012) Probabilistic seismic hazard assessment of India. Seismol Res Lett 83(1):135–149. doi:10.1785/gssrl.83.1.135v

    Article  Google Scholar 

  • Nath SK, Yadav AK (2014) Effect of time-dependence source model on the probabilistic Seismic Hazard of Guwahati: an appraisal. Bull Seismol Res Am (under review)

  • Ogata Y, Katsura K (1993) Analysis of temporal and spatial heterogeneity of magnitude frequency distribution inferred from earthquake catalogues. Geophys J Int 113:727–738

    Article  Google Scholar 

  • Öncel A, Wyss M (2000) The major asperities of the 1999 Mw = 7.4 Izmit earthquake defined by the microseismicity of the two decades before it. Geophys J Int 143:501–506

    Article  Google Scholar 

  • Ota K (2005) Seminar on b-value. Department of Geophysics, Charles University, Prague, December 10–19

  • Paolo B, Cornell CA (1999) Disaggregation of seismic hazard. Bull Seismol Soc Am 89(2):501–520

    Google Scholar 

  • Reynolds DA (1992) A Gaussian mixture modeling approach to text-independent speaker identification. Ph.D. thesis, Georgia Institute of Technology

  • Schorlemmer D (2004) Earthquake statistics at Parkfield: 1. Stationarity of b values. J Geophys Res 109(B12):B12307. doi:10.1029/2004JB003234

    Article  Google Scholar 

  • Schorlemmer D, Wiemer S, Wyss M (2004) Earthquake statistics at Parkfield: 1. Stationarity of b values. J Geophys Res. doi:10.1029/2004JB003234

    Google Scholar 

  • Smyth C, Mori J (2009) Assessing temporal variations in the Gutenberg-Richter distribution for a short-term forecast model, Japan Geoscience Union Meeting, Chiba, Japan

  • Smyth C, Mori J (2011) Statistical models for temporal variations of seismicity parameters to forecast seismicity rates in Japan. Earth Planets Space 63:231–238

    Article  Google Scholar 

  • Snedecor GW, William CG (1989) Statistical Methods, 8th edn. Lowa State University Press, Ames

    Google Scholar 

  • Tassara A, Soto H, Bedford J, Moreno M, Baez JC, Concepcion UD (2012) Contrasting postseismic behaviour of the megathrust after the Mw8.8 2010 Maule Earthquake: spatio-temporal variations of afterslip and seismicity, AGU Fall Meeting Abstracts, T31D-2623

  • Thomas H, Lohaus A (1993) Modeling growth and individual differences in spatial tasks. Monogr Soc Res Child Dev 58(9):1–191

    Article  Google Scholar 

  • Tiampo KF, Shcherbakov R (2012) Seismicity-based earthquake forecasting techniques: ten years of progress. Tectonophysics 522–523:89–121

    Article  Google Scholar 

  • Tormann T, Wiemer S, Hardebeck JL (2012) Earthquake recurrence models fail when earthquakes fail to reset the stress field. Geophys Res Lett. doi:10.1029/2012GL052913

    Google Scholar 

  • Tormann T, Wiemer S, Metzger S, Michael A, Hardebeck JL (2013) Size distribution of Parkfield’s microearthquakes reflects changes in surface creep rate. Geophys J Int. doi:10.1093/gji/ggt093

    Google Scholar 

  • Tsay RS (2002) Analysis of financial time series. Wiley, New York

    Book  Google Scholar 

  • Utsu T (1965) A method for determining the value of b in the formula log N = a − bM showing the magnitude–frequency relation for earthquakes. Geophys Bull Hokkaido Univ 13:99–103 (in Japanese with English abstract)

    Google Scholar 

  • Wiemer S, Benoit J (1996) Mapping the b-value anomaly at 100 km depth in the Alaska and New Zealand subduction zones. Geophys Res Lett 23:1557–1560

    Article  Google Scholar 

  • Wiemer S, Schorlemmer D (2007) ALM: an asperity-based likelihood model for California. Seismol Res Lett 78:134–140

    Article  Google Scholar 

  • Wiemer S, Wyss M (1997) Mapping the frequency–magnitude distribution in asperities: an improved technique to calculate recurrence times? J Geophys Res 102:15115–15128

    Article  Google Scholar 

  • Wiemer S, Wyss M (2002) Mapping spatial variability of the frequency–magnitude distribution of earthquakes. Adv Geophys 45:259–302

    Article  Google Scholar 

  • Wiemer S, McNutt SR, Wyss M (1998) Temporal and three-dimensional spatial analysis of the frequency–magnitude distribution. Geophys J Int 134:409–421

    Article  Google Scholar 

  • Woessner J, Wiemer S (2005) Assessing the quality of earthquake catalogues: estimating the magnitude of completeness and its uncertainties. Bull Seismol Soc Am 95(2):684–698. doi:10.1785/01200407

    Article  Google Scholar 

  • Wyss M, Wiemer S (2000) Change in the probability for earthquakes in Southern California due to the Landers magnitude 7.3 earthquakes. Science 290:1334–1338

    Article  Google Scholar 

Download references

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Correspondence to Arabind Kumar Yadav.

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Yadav, A.K. Long-term earthquake forecasting model for northeast India and surrounding region: seismicity-based model. Nat Hazards 80, 173–190 (2016). https://doi.org/10.1007/s11069-015-1963-8

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