Analyzing historical seismic data for region-specific earthquake prediction through deep neural networks

This study addresses the problem of improving the accuracy of earthquake fore casting in Kazakhstan using deep learning methods. Special attention is paid to forecasting in zones with increased seismic activity, which is a unique feature of the regional plan. The main objective of the work is to develop a model based on the architecture of deep neural networks with direct propagation of signals to analyze historical data containing the time of occurrence, geographic coordinates and magnitude of earthquakes. This model is executed as a basis for classification and regression, permitting assessment of the level of agreement between predicted values and those that were encountered. Evidence of earthquake prediction poten tial is shown through utilizing this methodology utilizing computational capacity as well as advanced neural network techniques. The significance of this study re sides in its contribution to the availability of processes and methods to facilitate the application of deep learning in the field of seismology to improve the accuracy and effectiveness of diagnosis and prevention of natural catastrophe.