Earthquake forecasting using traditional methods remains a complex task due to the inherent nonlinearity and stochastic nature of seismic activity. Therefore, this study examines the application of deep learning methods, particularly a hybrid model combining Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN), to create a robust prediction model that outperforms traditional methods. The study comprises six primary phases: data acquisition, pre-processing, exploratory analysis, seismicity characterization, model implementation, and effectiveness assessment. The dataset contained 17,565 earthquake events (M ≥ 3.0) in Central Asia, collected from 1973–2024 via the USGS API, enriched with geological features such as distance to active faults (from GEM datasets) and eight key seismicity indicators. Compared with baseline architectures (standard LSTM and fully connected neural network), the proposed CNN-LSTM hybrid achieved superior performance, particularly in recall (0.80 compared with 0.67) and F1-score (0.77 compared with 0.71), demonstrating its ability to capture both spatial and temporal dependencies. The results indicate that using a hybrid CNN-LSTM model leads to a significant improvement in forecasting strong earthquakes. This is validated by ROC-AUC of 0.78 and F1-score of 0.77, supporting a good balance between model completeness and accuracy. These improvements confirm that the regional adaptation of CNN-LSTM, combined with seismic indicators and clustering-based subregionalization, provides a novel methodological contribution to earthquake forecasting in Central Asia. The model reliably distinguishes periods of elevated seismic risk from quiet intervals, supporting practical applications in regional forecasting and seismic risk visualization.

Monitoring results for the period 2009–2014 by ten standard GPS stations allowed to determine the crustal movements of seismically active region in south-eastern Kazakhstan. Maps of movement velocity were made in geocentric coordinate system and in reference system of the Eurasian continent. GPS points displacements reflect the features of modern deformation processes that are notable in the high seismic activity region. The structure of the velocity field divergence qualitatively confirms major deformation in the sublatitudinal direction which is parallel to the main ridge of the northern Tianshan Mountain. The epicenters of earthquakes are in agreement with the border areas of compression–tension, as well as the allocated areas of multidirectional rotary motion. The conclusion is that GPS monitoring of the movements of the Earth's crust can be used to evaluate the territory's stress–strain state for the purpose of seismic zoning and seismic risk assessment

This study developed a copper mineral prospectivity map for the Koldar massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatialgenetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is thoroughly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality hyperspectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations.

This study developed a copper mineral prospectivity map for the Koldar Massif, Kazakhstan, using an integrated approach combining geophysical and satellite methods. A strong spatial-genetic link was identified between faults and hydrothermal mineralization, with faults acting as key conduits for ore-bearing fluids. Lineament analysis and density mapping confirmed the high permeability of the Koldar Massif, indicating its structural prospectivity. Hyperspectral and multispectral data (ASTER, PRISMA, WorldView-3) were applied for detailed mapping of hydrothermal alteration (phyllic, propylitic, argillic zones), which are critical for discovering porphyry copper deposits. In particular, WorldView-3 imagery facilitated the identification of new prospective zones. The transformation of magnetic and gravity data successfully delineated geological features and structural boundaries, confirming the fractured nature of the massif, a key structural factor for mineralization. The resulting map of prospective zones, created by normalizing and integrating four evidential layers (lineament density, PRISMA-derived hydrothermal alteration, magnetic, and gravity anomalies), is highly validated, successfully outlining the known Aktogay, Aidarly, and Kyzylkiya deposits. Furthermore, new, previously underestimated prospective areas were identified. This work fills a significant knowledge gap concerning the Koldar Massif, which had not been extensively studied using satellite methods previously. The key advantage of this research lies in its comprehensive approach and the successful application of high-quality multispectral imagery for mapping new prospective zones, offering a cost-effective and efficient alternative to traditional ground-based investigations.

The objective of this study is to quantify and characterize ground deformations induced by underground coal mining in the Karaganda coal basin, Kazakhstan, in order to improve the understanding of subsidence processes and their long-term evolution. The SBAS-InSAR method was applied to Sentinel-1 (C-band) and TerraSAR-X (X-band) data from 2019–2021 to estimate the magnitude, extent, and temporal behavior of displacements over the Kostenko, Kuzembayev, Aktasskaya, and Saranskaya mines. The results reveal spatially coherent and progressive deformation, with maximum cumulative LOS displacements exceeding –800 mm in TerraSAR-X data within active longwall mining zones. Time-series analysis confirmed acceleration of displacement during active extraction and its subsequent attenuation after mining ceased. Comparative assessment demonstrated a strong agreement between Sentinel-1 and TerraSAR-X results (r = 0.9628), despite differences in resolution and acquisition geometry, highlighting the robustness of the SBAS-InSAR approach. Analysis of displacement over individual longwalls showed that several panels (3, 5, 8, 15, and 18) already exceeded their projected maximum subsidence values, underlining the necessity of continuous monitoring for ensuring safety. In contrast, other longwalls have not yet reached their maximum deformation, indicating potential for further activity. Overall, this study demonstrates the value of multi-sensor InSAR monitoring for reliable assessment of mining-induced subsidence and for supporting geotechnical risk management in post-industrial regions.

The deep crustal temperatures of the Northern Tien Shan region are being calculated on the basis of direct measurements of the heat flow and empirical relation of geothermal parameters and seismic velocities. For this purpose the applied method of numerical solution of conductive heat transfer equation takes into account additional sources of heat release due to radioactive decay. Thermoelastic deformations, reaching 20-40% of the total lithostatic pressure at depths of 35-60 km, are determined. The criteria controlling the distribution of hypocenters of earthquakes have been determined in accordance with the morphology of the regions of excess temperatures and thermal stresses.