Abstract
With the gradual increase in mining depth of coal fields in North China, the threat posed by karstic Ordovician limestone water to the safe stoping of mines is becoming increasingly prominent. Investigating the water-resisting property of the filling zone on the top of the Ordovician limestone provides the key to safe mining under pressure. This paper analyzed the formation process of the filling zone on the top of Ordovician limestone in North China, and by combining analysis results of several geological field investigations on Ordovician outcrops, the filling zone on the top of Ordovician limestone was divided into three water-resisting structures: (1) completely filled, (2) incompletely filled, and (3) nonfilled. Based on the lithological composition, logging curves, and the water inflow status of several field boreholes, various characteristics of these clay-filled zones were used to determine the mudstone content from top to bottom. Using the interbedded mudstone thickness ratio, relative argillaceous content, impermeable filling zone thickness, rock quality designation (RQD), and faulting as evaluation factors, this paper evaluated the water-resisting property of the filling zone in the study area based on feature-weighted fuzzy C-means clustering (WFCM) algorithm and determined the extent of each zone. The completely filled zone accounts for 46.9% of the total area, incompletely filled zone accounts for 23.9%, and the zone not filled with clay material accounts for 29.2%. As indicated by field investigations on the boreholes, the actual percent of each zone is similar to the theoretical results. The study results present a vital guide for Ordovician limestone water control in deep mining.
Highlights
The Permo-Carboniferous system is the primary coalbearing formation in China [1,2,3], and the coal seams existing in its lower section are widely threatened by underlying karstic Ordovician limestone water
Fuzzy C-means algorithm uses the Euclidean distance dki = xk − ci between a sample data point and cluster center as the index measuring dissimilarity between the two and assumes that the multidimensional features of a cluster sample data point have equal importance for clustering; in actual situations, the contributions made by the multidimensional features of a data point to the clustering results are apparently different. With regard to this problem, the concept of feature weight was introduced in this paper into fuzzy C-means clustering to improve fuzzy C-means algorithm, obtaining feature-weighted fuzzy C-means clustering algorithm
The multidimensional features of the sample data points are weighted according to their degrees of importance, so as to strengthen the role of primary features in the clustering analysis, weaken the misleading effects of secondary features for clustering results, and improve the accuracy of clustering results
Summary
The Permo-Carboniferous system is the primary coalbearing formation in China [1,2,3], and the coal seams existing in its lower section are widely threatened by underlying karstic Ordovician limestone water. Studies on the filling zone on the top of Ordovician limestone mainly focus on the development of karsts [17, 19], covering the micromechanisms of karsts [20, 21], genetic mechanisms of karst caves and collapse columns [22, 23], water yield properties of karsts [24, 25], etc. They rarely deeply examine the differences among the filling zones in terms of structural characteristics, water-resisting property, etc. The distribution of this impermeable layer is important for safe mining in the region
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