Ryszard Hejmanowski

Application of Land Subsidence Inversion for Salt Mining-Induced Rock Mass Movement

Modelling of strains and deformations in salt mine areas encounters considerable difficulties because of the varying strength properties of salt, complex morphological formation of dome deposits and rheological properties of salt. Due to such properties the impact of salt extraction increases over hundreds of years and accurate determination of strains at a given moment and place is burdened with high uncertainty. Numerical modelling is useful when the model is reduced to one or several chambers. A broader range considerably lowers the accuracy and efficiency of calculations in such models. Stochastic models allow for 3D modelling of an entire mining complex, provided the model has been parametrized in detail. The process of strain and deformation modelling was presented on the example of one of the biggest salt mines in Europe, where the volume of over 21 million m3 of salt deposit was extracted. The stochastic model could be parametrized thanks to the documented measurements results of panel convergence and levelling on the surface. The use of land subsidence inversion in the least squares method allowed to estimate the optimum values of the model parameters. The correctness of the evaluation was qualitatively and quantitatively confirmed graphically by comparing modelled and measured values of subsidence. The presented model can be applied in the future extraction projects for predicting strains and deformations for an arbitrary moment

Selection of Parameters of the Support Vector Machine Method to the Problem of Subsidence Modelling Due to Drainage

This article presents the problem of modelling drainage subsidence that accompanies the mining of solid minerals. Rock mass drainage causes a change in pressure in the aquifer, and thereby initiates the compaction process. On the surface we can observe the effect in the form of a wide drainage basin, which adds to the direct impact of mining operations. The article presents the research stage associated with the use of artificial intelligence in forecasting the indirect impacts of (drainage) in mining areas. This article also outlines the Support Vector Machine (SVM) method and its use based on the example of underground coal mining. For the purpose of calculations, the data from altitude surveying conducted on the terrain surface, and information from the network piezometric boreholes installed in subsequent aquifers were used. Used in the analysis was e-SVM method for regression tasks with the use of radial basis function. The calculations were performed with an integrated software package for support vector regression (LIBSVM) and the obtained results were presented. The process of selection of parameters in different variants, and obtained discrepancies in the process of research and testing were described. Cross-Validation and generalization of the knowledge processes necessary for future forecasting the process of drainage subsidence were characterized. The summary includes opportunities for further research as well as analysis using artificial intelligence.