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.

Integrating CAD-based mine maps with GIS through ETL tools requires an intermediate spatial data format suitable for a range of applications. This industrial case study compares GeoJSON, GeoPackage and GML, each uncompressed and in ZIP archives, using production mine workings maps of three underground copper mines of KGHM Polska Miedź S.A. (1,179,967 vector features). The comparison was undertaken to support the enterprise’s internal mine map data integration needs. Eleven operations were timed in FME, GDAL/OGR and QGIS, covering local and network reading and writing as well as interactive display. Uncompressed GeoPackage was the fastest in nine of the eleven operations; in local reading in FME, it required 7.0 s, compared with 48.2 s for GeoJSON and 191.6 s for GML, and it was 24.76% and 39.94% smaller, respectively. ZIP compression reduced file sizes by 90.75–94.68%, with GeoJSON producing the smallest archive. Compression shortened network reading of GeoPackage and network writing of the text-based formats in FME (GeoJSON: from 991 to 172 s), but it slowed local reading and, for GeoPackage, zooming in QGIS (from 9 to 388 s). Working, transport and archival formats should therefore be selected against separate criteria, and format efficiency must be combined with change detection, concurrency control and limited disk operations in the ETL process.