Land subsidence caused by groundwater withdrawal induced by mining is a relatively unknown phenomenon. This is primarily due to the small scale of such movements compared to the land subsidence caused by deposit extraction. Nonetheless, the environmental impact of drainage-related land subsidence remains underestimated. The research was carried out in the “Bogdanka” coal mine in Poland. First, the historical impact of mining on land subsidence and groundwater head changes was investigated. The outcomes of these studies were used to construct the influence method model. With field data, our model was successfully calibrated and validated. Finally, it was used for land subsidence estimation for 2030. As per the findings, the field of mining exploitation has the greatest land subsidence. In 2014, the maximum value of the phenomenon was 0.313 cm. However, this value will reach 0.364 m by 2030. The spatial extent of land subsidence caused by mining-induced drainage extends up to 20 km beyond the mining area’s boundaries. The presented model provided land subsidence patterns without the need for a complex numerical subsidence model. As a result, the method presented can be effectively used for land subsidence regulation plans considering the impact of mining on the aquifer system.

The presented research aimed to evaluate the spatio-temporal distribution of ground movements caused by groundwater head changes induced by mining. The research was carried out in the area of one of the copper ore and anhydrite mines in Poland. To determine ground movements, classical surveying results and the persistent scatter Satellite Radar Interferometry (PSInSAR) method were applied. The mining operation triggered significant subsidence, reaching 1.4 m in the years 1944–2015. However, subsidence caused by groundwater pumping was about 0.3 m. After mine closure, an ongoing groundwater rebound was observed. Hence, land uplift occurred, reaching no more than 29 mm/y. The main part of the investigation concerned developing a novel method for uplift prediction. Therefore, an attempt was made to comparatively analyze the dynamics of ground movements correlated with the mine life and hydrogeological condition. These analyses allowed the time factor for the modeling of land uplift to be determined. The investigation also revealed that in the next six years, the uplift will reach up to 12 mm/y. The developed methodology could be applied in any post-mining area where groundwater-rebound-related uplift is observed.

Earthquake occurrence is usually unpredictable apart from sites in the vicinity of volcanoes. It is not easy to measure displacements caused by seismic phenomena using classical geodetic methods, which are based on point survey. Therefore, the surveying of ground movements caused by seismic events should be carried out continuously. Nowadays, remote sensing data and InSAR are often applied to monitor ground displacements in areas affected by seismicity. The effects of severe nearby mining-induced earthquakes have been discussed in the paper. The earthquakes occurred in 2017 and had a magnitude of 4.7 and 4.8. The distance between the epicenters of the mining-induced earthquakes was around 1.6 km. The aim of the investigation has been to analyze the spatio-temporal distribution of ground movements caused by the two tremors using the InSAR technique. Superposition of surface displacement has been studied in time and space. The main scientific aim has been to prove that in the areas where high-energy tremors occur, ground movements overlap. Due to proximity between the epicenters, the mining-induced earthquakes caused the formation of a large subsidence trough with the dimension of approximately 1.2 km × 4.2 km and total subsidence of ca. 116 mm. Two-time phases of subsidence were determined with temporal overlapping. The subsidence analysis has enhanced the cognition of the impact of mining-induced seismicity on the kinematics of surface changes. Moreover, the present work supports the thesis that InSAR is a valuable and adequately accurate technique to monitor ground displacements caused by mining induced earthquakes.

Horizontal strains related to mining-induced subsidence may endanger infrastructure and surface users’ safety. While directional horizontal strains should be well determined, appropriate solutions for a complete assessment of the terrain surface deformation field are still required. As a result, the presented study examined a new method for calculating horizontal strain tensor based on the decomposition of satellite radar interferometry (InSAR) observations into vertical and azimuth look direction (ALD) displacements. Based on a geometric integral model, we tested our method on experimental data before applying it to an underground copper ore mine in Poland. In the case study, the displacement field was determined using the Multi-Temporal InSAR method on Sentinel-1 data. The model data relative error did not exceed 0.02 at σ = ±0.003. For the case study, land subsidence of up to −167 mm and ALD displacements ranging from −110 mm to +62 mm was obtained, whereas the extreme values of horizontal strains ranged from −0.52 mm/m to +0.36 mm/m at σ = ±0.050 mm/m. Our results demonstrate the high accuracy of the method in determining the horizontal strain tensor. As a result, the approach can broaden the assessment of the environmental impact of land subsidence worldwide.

Based on the previous studies conducted by the authors, a new approach was proposed, namely the tools of artificial intelligence. One of neural networks is a multilayer perceptron network (MLP), which has already found applications in many fields of science. Sequentially, a series of calculations was made for different MLP neural network configuration and the best of them was selected. Mean square error (MSE) and the correlation coefficient R were adopted as the selection criterion for the optimal network. The obtained results were characterized with a considerable dispersion. With an increase in the amount of hidden neurons, the MSE of the network increased while the correlation coefficient R decreased. Similar conclusions were drawn for the network with a small number of hidden neurons. The analysis allowed to select a network composed of 24 neurons as the best one for the issue under question. The obtained final answers of artificial neural network were presented in a histogram as differences between the calculated and expected value