Coal mining inevitably brings some negative impacts, such as surface subsidence, aquifer breakage, and land degradation, to the eco-geological environment in the mining area. Among these impacts, coal mining-induced ground deformation is the most serious and has threatened the geological, ecological, and human settlement securities of mining areas. Efforts existing in the literature apply to ground deformation identification in mined-out areas at the meso-/micro and short-time scales. However, when looking back at coal mining history, there are few ways to quickly and accurately quantify ground deformation at the regional and long-time scales. In this context, we propose a method for identifying ground deformation patterns in coal mining areas using historical high-precision digital elevation models (DEMs), including data preprocessing, DEM subtraction operations, interpretation, and fitting correction. This method was applied to the Yulin National Energy and Chemical Base and successfully identified the ground deformation characteristics of the Yulin coal mining area from 2015 to 2019. By determining surface subsidence displacement, excavation depth, stacking height, and the position of the goaf suspended roof area, the objective situation of ground deformation in Yulin mining area was obtained, and the mining methods and distribution characteristics of different surface deformations were analyzed and determined. The research results are of great significance for the development of mineral resources in mining areas, reducing geological disaster risks, protecting the ecological environment, and achieving the goal of coordinated development in mining areas.

Loess regions face significant challenges in quantifying hydrological processes and assessing geological environmental risks due to the prevalent development of preferential pathways and the limitations of existing monitoring technologies. To advance this knowledge, this study presents an improved electrical resistivity tomography (ERT) device, specifically designed for loess moisture observations. By refining the testing principle, power supply mode, and data collection method within the existing ERT framework, the new device offers unmanned operation, automatic data acquisition, remote transmission, and cost efficiency. It effectively tracks water movement and groundwater level fluctuations across various hydrological conditions, supporting long-term online monitoring of hydrological processes of loess slopes. Through the analysis of monitoring data and classification of 12 observed preferential flow types, water movement in loess systems can be generalized into four general patterns: uniform infiltration, preferential infiltration, inflowing diffusion, and lateral flow. This generalized scheme provides a simplified modeling approach for other researchers to quantify slope hydrodynamics and to assess geological safety risks involving preferential flow. Based on these insights and field investigations, a conceptual framework is proposed to elucidate the seepage-structure synergistic initiating mechanism of loess landslides. This framework suggests that water entry and movement patterns within the slope depend on the slope geological structure related to preferential pathways and the prevailing hydrological scenarios. Landslide occurs as the result of the progressive failure and reciprocal evolution between the slope hydrological environments and geological structure, which may also pose potential eco-hydrological risks. The outcome advances the development of slope hydrological monitoring technology and enhances the understanding of water movement laws and the associated geological environmental risks in loess slope systems, which is of vital importance to the early warning methods of loess landslides that account for preferential flow and for theoretical modeling of preferential flow in related disciplines.