Lithological mapping using satellite images, particularly hyperspectral data, helps in effectively defining the best initial targets for regional exploration. In this study, ZY1-02D hyperspectral image (HSI) data with moderate spectral and very high spatial resolution were employed for lithological mapping using spectral indices along with support vector machine (SVM) machine learning and spatial–spectral transformer (SSTF) deep learning methods in the Kohat–Pothohar Plateau at the eastern edge of the Main Boundary Thrust (MBT) in Pakistan. The research was accomplished using spectral profiles of minerals accompanied by false color composite (FCC), principal component analysis (PCA), SVM, and SSTF methods for classifying the main lithological units. The lithological discrimination map derived from the ZY1-02D data matched well with the known deposits and field inspections. The principal component analysis (PCA) obtained the highest eigenvalues and provided a significant discrimination of lithologies, particularly with hyperspectral data. The results revealed lithological units, three of which contained limestone and gypsum, while other lithological units were defined as sandstone, clay, and conglomerates. Field investigation and laboratory sample analysis through X-ray diffraction (XRD), photomicrographs, and spectral analysis confirmed the occurrence of limestone, gypsum, and sandstone, which are useful in identifying lithological units in the study area. This study will assist in more accurate geological discrimination and play a vital role in identifying oil and gas reservoirs, coal, gypsum, uranium, salt, and limestone deposits. Furthermore, the results of the SVM and SSTF techniques were quantitatively compared with the geological boundaries mapped in the field, showing an accuracy of nearly 89.7% and 92.1%, respectively. Overall, the methodology adopted showed great performance and strong potential for mapping alteration areas and lithological discriminations applied on the ZY1-02D hyperspectral data.

In recent years, coal mining has shifted from surface to underground multi-seam and multi-panel operations, leading to enhanced ground deformation and elevated risks of secondary geo-hazards. However, the deformation mechanisms and spatiotemporal evolution of mining-induced ground movement in high-intensity repeated mining areas require further investigation. To gain further insight, this study focuses on elucidating the deformation mechanisms and hazard-chain evolution induced by downward multi-seam and multi-panel mining in the Hongyan coal mine, located in the loess gully region. An integrated InSAR-based multi-source monitoring and modeling framework was adopted, systematically combining InSAR, historical satellite imagery, UAV-based surveys, and ground observations with numerical simulations to characterize the spatiotemporal evolution of mining-induced deformation and examine the coupling processes within the hazard chain. The monitoring results show a strong spatiotemporal correlation between mining activities and ground deformation: subsidence basins and temporal variations correspond closely to the mining sequence, and the spatial distribution of fissures aligns with the advancing working faces. The analysis indicates mining-induced stress redistribution and stratum instability are the root causes of subsidence. Subsidence characteristics are affected by topography, mining sequence, and the cumulative impacts of multi-seam mining, leading to stepwise subsidence and subsidence basins. The overlying loess’s topography and characteristics affect the subsidence distribution. The “stress arch” formed in the goaf evolves with the multi-panel mining process, gradually collapsing during continuous mining and leading to stratum instability. Initially spreading stress and preventing rock movement, the upper residual pillars aggravate stratum damage following critical stratum failure. Mining exerts spatiotemporal control over hazard development, with the hazard chain evolving upward from the mining horizon, driven by fissure propagation and subsidence as the core processes, and reinforced by a bottom-up chain reaction and feedback among successive hazards. This study provides scientific insights for the planning and hazard prevention of multi-seam mining in loess gully regions.