Application of Neural Network Technologies for Tectonic Earthquake Forecast

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Abstract

Successful earthquake prediction includes statistical, tectonic and physical forecasting. The main requirements for this are the establishment of the laws of earthquake mechanics and control of the geodynamic state in the region at the right times. However, resolving this issue faces difficulties of both theoretical and practical nature. Despite the fact that specialists all over the World have collected the fairly complete database on earthquakes, tectonic, electromagnetic, hydrological, etc. signs of earthquakes, the very nature of predicting the future source remains uncertain. The results obtained in the world on statistical forecasting using artificial intelligence give hope for the possibility of predicting earthquakes if we combine tectonic forecasting with the destruction of materials under experimental conditions and numerical modeling under the roof of deep learning neural network technologies. The paper provides the first results of predicting medium-term tectonic earthquakes using artificial intelligence for the Fergana depression in Uzbekistan.

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About the authors

I. U. Atabekov

Mavlyanov Institute of Seismology, Academy of Sciences of Republic of Uzbekistan

Author for correspondence.
Email: atabekovi@mail.ru
Uzbekistan, bld. 3, st. Zulfiyakhanum, 100028 Tashkent

A. I. Atabekov

Digital Technology and Artificial Intelligence Research Institute, Ministry of Digital Technologies of Republic of Uzbekistan

Email: atabekovi@mail.ru
Uzbekistan, bld. 17A, BUZ-2, 100125 Tashkent

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2. Fig. 1. Topographic map of the Fergana Depression and its surroundings (according to [22]).

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3. Fig. 2. The territory of the Fergana Depression, divided into 17 zones, with active faults indicated. Seismic events are shown: real (red circles); accurately predicted by the LSTM algorithm (green circles).

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