RESUMO
Clastic rock aquifer of the coal seam roof often constitutes the direct water-filling aquifer of the coal seam and its water-richness is closely related to the risk of roof water inrush. Therefore, the evaluation of the water-richness of clastic rock aquifer is the basic work of coal seam roof water disaster prevention. This article took the 4th coal seam in Huafeng mine field as an example. It combined the empirical formula method and generalized regression neural network (GRNN) to calculate the development height of water-conducting fracture zone, determined the vertical spatial range of water-richness evaluation. Depth of the sandstone floor, brittle rock ratio, lithological structure index, fault strength index, and fault intersections and endpoints density were selected as the main controlling factors. A combination weighting method based on the analytic hierarchy process (AHP), rough set theory (RS), and minimum deviation method (MD) was proposed to determine the weight of the main controlling factors. Introduced the theory of unascertained measures and confidence recognition criteria to construct an evaluation model for the water-richness of clastic rock aquifers, the study area was divided into three zones: relatively weak water-richness zones, medium water-richness zones, and relatively strong water-richness zones. By comparing with the water inrush points and the water inflow of workfaces, the evaluation model's water yield zoning was consistent with the actual situation, and the prediction effect was good. This provided a new idea for the evaluation of the water-richness of the clastic rock aquifer on the roof of the mining coal seam.
RESUMO
Identifying the water richness of coal roof aquifers is an important and difficult goal of hydrogeological research to prevent and control roof water disasters. To evaluate the water richness of roof sandstone aquifers of the No. 1 coal seam in the Changcheng No. 1 coal mine, a multifactor prediction method based on the fuzzy Delphi analytic hierarchy process (FDAHP), entropy weight method (EWM), sum of squared deviations (SSD), and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was proposed. Multisource geological data, including sandstone thickness, burial depth, lithological composition index, core recovery, fault scale index, fault intersections and endpoint density, and fold fractal dimension, were chosen as the primary indicators for evaluating the water richness of roof sandstone aquifers. The FDAHP and EWM were used to scientifically determine the subjective and objective weight vectors of these seven main factors, and the SSD was used to determine the optimal combination weights based on the objective and subjective weight vectors. On this basis, the water richness index (WRI) model was developed using the TOPSIS method to rank the water richness of samples in the study area. A water richness zoning map was created using the WRI values, revealing three zones: the weak water richness zone, moderate water richness zone, and strong water richness zone. Additionally, the map was refined by incorporating hydrogeologic data collected during mining operations, including pumping tests and actual water inrushes from roadways and working faces. It is believed that the proposed WRI model is effective for predicting the water richness of the roof sandstone aquifers of the No. 1 coal seam in the Changcheng No. 1 coal mine based on the engineering practice data used to validate the WRI model.
