Underground and opencast mining adversely affects the surrounding environment. This process may continue even decades after the end of actual mineral extraction. One of the most significant effects of ceased mining are secondary deformations. Safe, new development of post-mining areas requires reliable information on potential deformation risk zones, which may be difficult to obtain due to a lack of necessary data. This study aimed to investigate and understand the secondary deformation processes in the underground mining area of the former “Babina” lignite mine, located in the unique glaciotectonic environment of the Muskau Arch, in western Poland. A combination of GIS-based historical mapping, geophysical 2D/3D microgravimetry, and Electrical Resistivity Tomography (ERT) measurements allowed the identification of subsidence-prone areas and the determination of potential factors of sinkhole development. The latter are associated with anthropogenic transformation of rock mass and hydrogeological conditions, by shallow underground mining. The results confirmed that multi-level mining of coal deposits in complex and complicated glaciotectonic conditions cause discontinuous deformations, and may be hazardous as long as 50 years after the end of mining operations.

A calcite deposit (stalagmite) from the Godarville tunnel (Belgium) was investigated by the use of x-ray and electron microprobes in order to detect any seasonal variations present in the chemical data and to make a comparison with meteorological data. The uniqueness of this speleothem system is that it is human constructed, controlled from the chronological point of view and with complete temperature and hydrological documentation.

Mining operations cause negative changes in the environment. Therefore, such areas require constant monitoring, which can benefit from remote sensing data. In this article, research was carried out on the environmental impact of underground hard coal mining in the Bogdanka mine, located in the southeastern Poland. For this purpose, spectral indexes, satellite radar interferometry, Geographic Information System (GIS) tools and machine learning algorithms were utilized. Based on optical, radar, geological, hydrological and meteorological data, a spatial model was developed to determine the statistical significance of the selected factors’ individual impact on the occurrence of wetlands. Obtained results show that Normalized Difference Vegetation Index (NDVI) change, terrain height, groundwater level and terrain displacement had a considerable influence on the occurrence of wetlands in the research area. Moreover, the machine learning model developed using the Random Forest algorithm allowed for an efficient determination of potential flooding zones based on a set of spatial variables, correctly detecting 76% area of wetlands. Finally, the GWR (Geographically Weighted Regression (GWR) modelling enabled identification of local anomalies of selected factors’ influence on the occurrence of wetlands, which in turn helped to understand the causes of wetland formation.