Application of Neural Network Technologies for Tectonic Earthquake Prediction
Abstract
Successful earthquake prediction includes statistical, tectonic, and physical forecasting. The main requirements for this are establishment of the laws of earthquake mechanics and monitoring of the geodynamic state in the region at the right times. However, resolving this issue faces both theoretical and practical difficulties. Although specialists worldwide have collected a fairly complete database on earthquakes and tectonic, electromagnetic, hydrological, etc., signs of earthquakes, the very nature of predicting a 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 tectonic forecasting is combined with destruction of materials under experimental conditions and numerical modeling under the roof of deep learning neural network technologies. The article provides the first results of predicting medium-term tectonic earthquakes using artificial intelligence for the Fergana Depression in Uzbekistan.