Research on water inrush early warning of coal seam floor based on naive Bayes classification

Water inrush of coal seam floor is usually accompanied by the upward extension of fractures in coal seam floor and the filling of fractures by confined water. Time-lapse electrical resistivity tomography (TL-ERT) is sensitive to low-resistivity anomalous bodies such as water, so it can be used to realize dynamic tracking of the upward extension process of water-bearing fractures. However, conventional TL-ERT methods cannot determine which points in the measured profile are the risk points of water inrush, so a naive Bayes classification algorithm based on pseudo-random matrix is proposed to solve this problem. By using this method, the probability contour map of the measured coal seam floor can be obtained, and the measured point whose probability value exceeds the threshold value of water inrush risk will be warned. Physical simulations show that the probability contour map generated by the proposed method can effectively display high-risk areas. The actual monitoring is carried out on a coal mining face, and the threshold value of water inrush risk is calculated as 0.45 according to the water inrush coefficient. Since there is no measured point whose probability value exceeds the threshold value, there is no need to issue water inrush early warning.