Machine Learning Methods for Seismic Hazards Forecast
The Institute for Information Transmission Problems, Moscow 127051, Russia
Author to whom correspondence should be addressed.
Received: 25 April 2019 / Revised: 9 July 2019 / Accepted: 10 July 2019 / Published: 12 July 2019
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In this paper, we suggest two machine learning methods for seismic hazard forecast. The first method is used for spatial forecasting of maximum possible earthquake magnitudes (
), whereas the second is used for spatio-temporal forecasting of strong earthquakes. The first method, the method of approximation of interval expert estimates, is based on a regression approach in which values of
at the points of the training sample are estimated by experts. The method allows one to formalize the knowledge of experts, to find the dependence of
on the properties of the geological environment, and to construct a map of the spatial forecast. The second method, the method of minimum area of alarm, uses retrospective data to identify the alarm area in which the epicenters of strong (target) earthquakes are expected at a certain time interval. This method is the basis of an automatic web-based platform that systematically forecasts target earthquakes. The results of testing the approach to earthquake prediction in the Mediterranean and Californian regions are presented. For the tests, well known parameters of earthquake catalogs were used. The method showed a satisfactory forecast quality.
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MDPI and ACS Style
Gitis, V.G.; Derendyaev, A.B. Machine Learning Methods for Seismic Hazards Forecast. Geosciences 2019, 9, 308.
Gitis VG, Derendyaev AB. Machine Learning Methods for Seismic Hazards Forecast. Geosciences. 2019; 9(7):308.
Gitis, Valeri G.; Derendyaev, Alexander B. 2019. "Machine Learning Methods for Seismic Hazards Forecast." Geosciences 9, no. 7: 308.
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