A method for identifying faults within mining faces based on spatial statistics

Minor faults within mining faces of coal mines severely affect gas drainage and coal seam mining. Accurately identifying parameters such as positions, throws, and attitudes of these faults is of great significance for the safe production of coal mines. To effectively reduce gas emissions, prevent gas explosions, and exploit and utilize gas resources, many gas drainage holes have been drilled during the construction of coal mines, providing favorable engineering conditions for identifying minor faults within coal seams. Compared with the conventional fault identification methods, which rely on the expertise of geologists, the identification model based on mathematical statistics and space fitting enjoys a high degree of automation. Therefore, based on the characteristics that two walls of a fault show different elevations and that coal seams have similar burial depths before being offset, as well as the data from gas drainage holes, this study proposed a philosophy for identifying minor faults within mining faces using the cluster analysis method. By comparing principles and structures of different clustering algorithms, this study built a model for identifying minor-faults in coal seams based on the K-Means clustering algorithm. The key technical process is as follows: the optimal number of clusters was determined first using the elbow method; with the Davies-Bouldin index and the correlation coefficients as the criteria for the evaluation of identification accuracy, minor faults in coal seams were finally identified using technological means such as anomalous point identification, the calculation of fault parameters (strikes, dip angles, and throws), fault plane fitting, and 3D visualization were employed. Using the identification model proposed in this study, faults with throws of 3 m and 1 m were identified on site using the data from partial gas drainage holes in the bottom drainage roadway of mining faces. As indicated by the comparative analysis of the faults revealed on site and the visualization results of the mining faces, the faults revealed on site are consistent with the results calculated using the model. Therefore, the identification method proposed in this study can be employed to identify faults within the mining faces of coal seams.