Determining Locations of Possible Earthquakes in the Western Tien Shan Using an Artificial Neural Network and a Mathematical Model of Tectonic Processes

In this article, we constructed a numerical model of the stress state of the Earth’s crust of the We-stern Tien Shan microplate to use as additional parameter for machine learning. An alternative to the deep learning models could be a neural network based on the Kolmogorov–Arnold general approximation theorem (KAN). What distinguishes a KAN from existing machine learning networks is its interpretability, i.e., the ability to explain the “logic” of the model’s operation and high accuracy in complex physical processes. In contrast to conventional networks, a KAN requires only one or two layers to obtain a solution to the problem, which significantly reduces computing power. Using the KAN algorithm, we have constructed for the first time a neural network for classification and regression applied to the medium-term earthquake prediction in the Western Tien Shan microplate. The results obtained allowed us to predict the locations of possible earthquakes with a magnitude of 5 > M < 6 in environs of the city Tashkent (the capital of the Republic of Uzbekistan). The performed retrospective analysis of strong earthquakes that occurred in 2024 within the West Tien Shan microplate showed that the developed model predicts the locations of earthquakes with a magnitude of M < 6 with an accuracy of geographic coordinates of ±0.1° N, ±0.1° E and a magnitude of ΔM = ±0.4.