Application of Neural Network Technologies for Tectonic Earthquake Forecast
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.