Preliminary results on variation of geophysical parameters during preparation of seismic events in Georgia using Machine Learning tools

The territory of Caucasus is a seismo-active region affected by the tectonic interaction of Arabian and Eurasian plates. The strong deformation processes developed here cause the accumulation of tectonic energy - stress, which discharges by the occurrence of numerous earthquakes. The monitoring and study of earthquake precursors represent the task of global importance.

It is known that there are number of earthquakes’ precursors, which can be registered in various geophysical fields (geomagnetic, hydrogeodeformation), but in order to consider the precursors registered before the activation of tectonic processes as a reliable earthquake indicators, it is necessary to reveal the solid connection between the seismic activity and the variation of the parameters, characterizing various geophysical fields. 

      The existing modern multiparametric monitoring system in Georgia, allow us to conduct a probabilistic assessment of expected earthquake magnitudes in different locations across Georgia, using modern Machine Learning (ML) methods, namely deep neural networks (DNN) technology, applied to experimental monitoring data on water level in boreholes and geomagnetic data.

During observation we consider the earthquake forecast as a binary problem of machine learning on the imbalanced data base applied to five regions of Georgia. For the training we used the geophysical data base collected in 2020-2024, namely, variations of statistical characteristics of geomagnetic field components, seismic activity, water level in deep boreholes and tides.

 In the present study, special attention is paid to the identification of stable precursor patterns by integrating multiple geophysical parameters within a unified analytical framework. Feature engineering and normalization techniques were applied to reduce noise and enhance the sensitivity of weak pre-seismic signals. The performance of the developed ML models was evaluated using standard classification metrics, including precision, recall, F1-score, and probability gain, demonstrating an improvement in detection capability compared to single-parameter approaches. The preliminary results indicate that joint analysis of geomagnetic, hydrogeological, and tidal data increases the reliability of probabilistic seismic forecasting and provides a promising basis for the development of an operational early-warning support system for seismically active regions of Georgia.