Open access to SAR data from the Sentinel 1 missions allows analyses of long-term ground surface changes. The current data-acquisition frequency of 12 days facilitates the continuous monitoring of phenomena such as volcanic and tectonic activity or mining-related deformations. SAR data are increasingly also used as input data in forecasting phenomena on the basis of machine learning. This article presents the possibility of using selected machine learning algorithms in forecasting the influence of underground mining activity on the ground surface. The study was performed for a mining protective area with a surface of over 500 km${}^2$ and located in western Poland. The ground surface displacements were calculated for the period from November 2014 to July 2021, with the use of the Small Baseline Subset (SBAS) method. The forecasts were performed for a total of 22 identified subsidence troughs. Each of the troughs was provided with two profiles, with a total of more than 10,000 identified points. The selected algorithms served to prepare 180-day displacement forecasts. The best results (significantly better than the baseline) were obtained with the ARIMA and Holt models. Linear models also provided better results than the baseline and their performance was very good at up to 2 months forecasting. Tree-based models including their sophisticated ensemble versions: bagging (Random Forest, Extra Trees) and boosting (XGBoost, LightGBM, CatBoost, Gradient Boosting, Hist Gradient Boosting) cannot be used for this type of predictions since Decision Trees are not able to extrapolate and thus are not a valid stand-alone tool for forecasting in this type of problems. A combination of satellite remote sensing data and machine learning facilitated both the simultaneous quasi-permanent monitoring of ground surface displacements and their forecasting in a relatively long time period.

The article presents the results of significance analyses of selected mining and geological variables for an area of underground mining activity. The study area was a region of an underground copper ore mine located in southwest Poland. The input data consisted of satellite radar data from the Sentinel 1 mission as well as mining and geological data. The line-of-sight subsidence, calculated with the use of the small baseline subset method and arranged in time series, was decomposed to extract the vertical component. The significance analysis of individual variables for the observed surface subsidence was performed using the SHapley Additive exPlanations method for the XGBoost machine learning model. The results of the analysis showed that the observed ground surface subsidence velocities were most influenced by the thickness of the PZ3 layer, which is located approximately 200 m above the roof of the mined seam, the thickness of the seam, and the timing of mining. It was also found that the proposed model was able to detect a nonlinear relationship between the analyzed excavations. The most significant influence on ground subsidence over mine excavations are mining parameters such as the spatially averaged thickness of the deposit and the time since liquidation of the deposit. The proposed approach can be successfully employed in planning both mining operations and mine closure in such a manner that the environmental impact is minimized.

EQ-INSAR is an open-source Python package that generates labeled synthetic InSAR deformation datasets from earthquake sources, addressing the shortage of annotated training data for machine learning (ML) applications in earthquake detection and induced seismicity monitoring. The package implements the Davis (1986) elastic half-space model to compute ground displacement from double-couple mechanisms. The resulting displacement field is projected onto the satellite line-of-sight (LOS) to generate interferometric phase maps and binary segmentation labels. The package supports nine satellite geometries, and a batch generator produces stacked (N,T,H,W) arrays for deep-learning frameworks. Validation against the 2021 Yangbi Mw,6.1 earthquake reduces the root-mean-square misfit from 2.02 cm to 0.86 cm (57% reduction) compared with the USGS catalog solution. EQ-INSAR is publicly available via PyPI and Zenodo.

EQ-INSAR is an open-source Python package for generating synthetic Interferometric Synthetic Aperture Radar (InSAR) surface deformation data from earthquake sources. The package implements the Davis (1986) point source model to compute three component ground displacement from double-couple earthquake mechanisms. Then it projects the displacement onto satellite line-of-sight (LOS) geometries to produce interferometric phase maps and binary segmentation labels suitable for machine learning. EQ-INSAR supports 9 pre configured SAR satellite geometries and includes C-band, L-band, and X-band, and provides batch generation of randomized training datasets. Real data validation against the 2021 Yangbi Mw 6.1 earthquake demonstrates sub centimeter RMS fit to observed Sentinel-1 InSAR data and independent recovery of the deformation centroid to within 6km of the Global CMT solution. The software is designed for researchers developing Machine Learning based InSAR analysis tools, educators teaching InSAR interpretation, and scientists requiring synthetic benchmarks for natural and induced seismicity studies.