Conference Paper

ARTIFICIAL INTELLIGENCE IN MODELLING OF SURFACE SUBSIDENCE DUE TO WATER WITHDRAWAL IN UNDERGROUND MINING

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... Analysis of measurement results indirectly describes the properties of reservoir rocks [9][10][11]. By comparing such data with rock mass geology, a connection may be found between the cause of the movement and its outcomes on the surface [12][13][14]. The parameters can be determined with a selected mathematical model, and a tool for predicting the future consequences of mining activity may be developed. ...
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Article
The paper presents a computer program called SubCom v1.0 for determining mathematical model parameters of compaction layers in areas of oil, gas or groundwater extraction. A stochastic model based on the influence function was used to model compaction and subsidence. Estimation of the model parameters was based on solving the inverse problem. Two model parameters were determined: the compaction coefficient Cm of reservoir rocks, and the parameter tgβ, which indirectly describes the mechanical properties of the overburden. The calculations were performed on leveling measurements of land subsidence, as well as on the geometry of the compaction layer and pressure changes in aquifers. The estimation of model parameters allows the prediction of surface deformations due to planned fluid extraction. An algorithm with a graphical user interface was implemented in the Scilab environment. The use of SubCom v1.0 is presented using the case of an underground hard coal mine. Water drainage from rock mass accompanying coal extraction resulted in compaction of the aquifer, which in turn led to additional surface subsidence. As a result, a subsidence trough occurred with a maximum subsidence of 0.56 m.
... The analyses utilised two designated states of the drainage basin falling for the years 1982 and 2012, and which were observed in one of the Polish deep mines. In addition data (Hejmanowski and Witkowski, 2015;Witkowski, 2015) from the piezometric holes installed in the four aquifers combined with geological information were used to execute simulation using artificial neural networks (training data). The obtained results were compared each time with the results of measurements from the observation line subjected only to the impact of water withdrawal (test data). ...
Chapter
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 ε-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.
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