Abstract
The systematic forecast of earthquakes is regularly performed with a step of \(\varDelta t\) in a predetermined analysis zone. At each step, the training set of target earthquakes is augmented, new data about the seismic process is loaded, the data is processed, transformed into grid-based fields, and the machine learning program calculates a solution using all available data from the beginning of the training to the moment \(t^*\) of the earthquake forecast. The result is a map of the alarm zone in which the epicenter of the target earthquake is expected in the interval \((t^*,t^*+\varDelta t]\). At the next step, the learning interval is increased \(\varDelta t\).
Usually, the quality of the forecast is estimated by the percentage of detection of target events for a given average value of the alarm zone. Here, we consider a generalization of the method of minimum area of alarm, designed to improve another characteristic of the forecast quality: the probability of occurrence of at least one target event in the expected alarm zone. The difference between the methods is that at the moment of forecasting \(t^*\) two decisions are made: forecast in time and in space. The first solution determines the possibility of an earthquake epicenter with a target magnitude in the forecast interval. If it is decided that the target event is possible, then a map with an alarm zone is calculated. A target earthquake is predicted if its epicenter falls into the calculated alarm zone.
Predictive modeling is carried out for California. The initial data are earthquake catalog and GPS time series. The results showed rather high estimates of the probability of detecting target events and very small values of estimates of the probability of predicting the appearance of the epicenter of a target event in the alarm zone. At the same time, the estimates of the probability of a systematic forecast are much higher than the similar forecast probabilities for random fields.
The paper is supported by the Russian Science Foundation, project No20-07-00445.
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References
Alves, E.I.: Earthquake forecasting using neural networks: results and future work. Nonlinear Dyn. 44(1), 341–349 (2006)
Amei, A., Fu, W., Ho, C.H.: Time series analysis for predicting the occurrences of large scale earthquakes. Int. J. Appl. Sci. Technol. 2(7) (2012)
Asim, K.M., Idris, A., Iqbal, T., Martínez-Álvarez, F.: Earthquake prediction model using support vector regressor and hybrid neural networks. PLoS ONE 13(7), e0199004 (2018)
Barnhart, W.D., Hayes, G.P., Wald, D.J.: Global earthquake response with imaging geodesy: recent examples from the USGS NEIC. Remote Sens. 11(11), 1357 (2019)
Bishop, C.M.: Machine learning and pattern recognition. In: Information Science and Statistics. Springer, Heidelberg (2006). ISBN:978-1-4939-3843-8
Blewitt, G., Hammond, W.C., Kreemer, C.: Harnessing the GPS data explosion for interdisciplinary science. Eos 99(10.1029), 485 (2018)
Corbi, F., et al.: Machine learning can predict the timing and size of analog earthquakes. Geophys. Res. Lett. 46(3), 1303–1311 (2019)
Geller, R.J., Jackson, D.D., Kagan, Y.Y., Mulargia, F.: Earthquakes cannot be predicted. Science 275(5306), 1616 (1997)
Gitis, V., Derendyaev, A.: The method of the minimum area of alarm for earthquake magnitude prediction. Front. Earth Sci. 8, 482 (2020)
Gitis, V., Derendyaev, A., Petrov, K.: Analyzing the performance of GPS data for earthquake prediction. Remote Sens. 13(9), 1842 (2021)
Gitis, V.G., Derendyaev, A.B.: Machine learning methods for seismic hazards forecast. Geosciences 9(7), 308 (2019)
Gufeld, I.L., Matveeva, M.I., Novoselov, O.N.: Why we cannot predict strong earthquakes in the earth’s crust. Geodyn. Tectonophys. 2(4), 378–415 (2015)
Keilis-Borok, V., Soloviev, A.A.: Nonlinear dynamics of the lithosphere and earthquake prediction. Springer Science & Business Media (2013)
Khan, Shehroz S.., Madden, Michael G..: A survey of recent trends in one class classification. In: Coyle, Lorcan, Freyne, Jill (eds.) AICS 2009. LNCS (LNAI), vol. 6206, pp. 188–197. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-17080-5_21
King, C.Y.: Gas geochemistry applied to earthquake prediction: an overview. J. Geophys. Res. Solid Earth 91(B12), 12269–12281 (1986)
Koronovsky, N., Naimark, A.: Earthquake prediction: is it a practicable scientific perspective or a challenge to science? Mosc. Univ. Geol. Bull. 64(1), 10–20 (2009)
Kossobokov, V., Shebalin, P.: Earthquake prediction. In: Nonlinear Dynamics of the Lithosphere and Earthquake Prediction, pp. 141–207. Springer (2003). https://doi.org/10.1007/978-3-662-05298-3
Kotsiantis, S.B., Zaharakis, I., Pintelas, P.: Supervised machine learning: a review of classification techniques. Emerg. Artif. Intell. Appl. Comput. Eng. 160, 3–24 (2007)
Kremer, N.S.: Probability theory and mathematical statistics. YUNITI-DANA, M p. 573 (2004)
Lighthill, J.: A critical review of VAN: earthquake prediction from seismic electrical signals. World scientific (1996)
Marzocchi, W., Zechar, J.D.: Earthquake forecasting and earthquake prediction: different approaches for obtaining the best model. Seismol. Res. Lett. 82(3), 442–448 (2011)
Matsumoto, N., Koizumi, N.: Recent hydrological and geochemical research for earthquake prediction in Japan. Nat. Hazards 69(2), 1247–1260 (2013)
Mignan, A., Broccardo, M.: Neural network applications in earthquake prediction (1994–2019): meta-analytic and statistical insights on their limitations. Seismol. Res. Lett. 91(4), 2330–2342 (2020)
Moustra, M., Avraamides, M., Christodoulou, C.: Artificial neural networks for earthquake prediction using time series magnitude data or seismic electric signals. Expert Syst. Appl. 38(12), 15032–15039 (2011)
Murai, S.: Can we predict earthquakes with GPS data? Int. J. Digital Earth 3(1), 83–90 (2010)
Panakkat, A., Adeli, H.: Neural network models for earthquake magnitude prediction using multiple seismicity indicators. Int. J. Neural Syst. 17(01), 13–33 (2007)
Priambodo, B., Mahmudy, W.F., Rahman, M.A.: Earthquake magnitude and grid-based location prediction using backpropagation neural network. Knowl. Eng. Data Sci. 3(1), 28–39 (2020)
Rhoades, D.A.: Mixture models for improved earthquake forecasting with short-to-medium time horizons. Bull. Seismol. Soc. Am. 103(4), 2203–2215 (2013)
Shebalin, P.N., Narteau, C., Zechar, J.D., Holschneider, M.: Combining earthquake forecasts using differential probability gains. Earth, Planets and Space 66(1), 1–14 (2014). https://doi.org/10.1186/1880-5981-66-37
Sobolev, G.: Principles of earthquake prediction (1993)
Sobolev, G., Ponomarev, A.: Earthquake physics and precursors. Publishing house Nauka, Moscow.-2003 (2003)
Zavyalov, A.: Intermediate term earthquake prediction. Nauka, Moscow. In: Zhang, L.Y., Mao, X.B., Lu, A.H. (eds.) (2009) Experimental Study of the Mechanical Properties of Rocks at High Temperature, Sci. China Ser. E, vol. 52(3), pp. 641–646 (2006)
Acknowledgements
This work was partially supported by RFBR grant 20-07-00445. The authors are grateful to E.N. Petrova and S.A. Pirogov for their helpful remarks.
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Gitis, V.G., Derendyaev, A.B., Petrov, K.N. (2022). On the Applied Efficiency of Systematic Earthquake Prediction. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13379. Springer, Cham. https://doi.org/10.1007/978-3-031-10545-6_41
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