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The existence of concealed faults not only decreases the production efficiency of a coal mine but also wastes resources and increases the risk of mine disasters. In this study, a method was developed to predict concealed faults in front of a coalface. The spatial distribution law of faults developed in the study area was characterized using the locations and attributes of fault zones, which can be determined by learning the strikes and locations of the faults with the K-means algorithm. Then, the concealed faults in front of coalfaces can be predicted by extending the fault zones along their strikes to unmined areas within the study area. Three attributes of fault zones, including extending index, buffer radius, and average throw, were defined and calculated to provide a quantitative evaluation of prediction results. The extending index represented the existence probability of the predicted fault. The buffer radius denoted the possible offset of the actual exposure point relative to the predicted location. The average throw gave the throw of the predicted fault. The method could also provide dynamic prediction as mining works were going on. Finally, the method was applied in mining region 302 of the Yanzishan Coal Mine in north China, and it was illustrated to be effective. In the test, the faults successfully predicted accounted for 82%, 89% of which was located within the range of buffer radius and also 89% had throw errors less than 50%.

The elevation interpolation of coal seam floors is an indispensable geological work to the production of coal mines. In this study, an improved radial basis function (RBF) interpolation method was proposed to support the interpolation of a feature with the local anisotropy characteristic. First, the local anisotropy of the floor elevation of coal seams was characterized by using the method of geographically weighted regression. The interpolation was realized by fitting residuals between actual values and geographically weighted regression values with RBF. Second, an adaptive selection of interpolation reference points was realized. The Delaunay triangulation network was used to define the relationship of natural proximity between the point to be interpolated and reference points and it ensured the relatively uniform distribution of reference points around the point to be interpolated. Based on this, the appropriate selection radius of reference points for the study area was obtained by cross validation. Third, the influence of faults was considered in the interpolation, and the value of reference points were augmented or deducted according to the attributes of faults concerned. Finally, the method was applied in the mining region 302 of Yanzishan Coal Mine in North China. The proposed method was illustrated to be effective with better robustness and interpolation accuracy compared with some typically used methods.