Zonal prediction of the heights of water-conducting fracture zones under varying overburden types in North China-type coalfields

BackgroundThe gradual increase in the exploitation depth and intensity of coal resources in the North China-type coalfields has caused problems such as overburden movement and damage, as well as fracture evolution. These hot topics have attracted considerable attention. MethodsA total of 117 sets of measured data on the heights of water-conducting fracture zones in the North China-type coalfields were collected, consisting of 17 sets from hard overburden, 42 sets from moderately hard overburden, and 58 sets from soft overburden. Using these data, this study explored the effects of varying mining heights, mining depths, and lengths of the mining face along its dip direction on the heights of water-conducting fracture zones under varying overburden types. Given the sedimentary characteristics of their coal-bearing strata, the North China-type coalfields were divided into three regions: the northern, middle, and southern belts. This study thoroughly investigated the heights of water-conducting fracture zones in the three regions using the convolutional neural network, the Bayes' formula, and empirical formulas for coal mining under buildings, water bodies, and railways. Results and ConclusionsThe results indicate that as the mining height, mining depth, or the length of the mining face along its dip direction varied, the height distributions of water-conducting fracture zones differed significantly under different overburden types. From hard, to medium-hard, and then to soft overburden types, the ratio of the height of water-conducting fracture zones to the mining height (also referred to as the fracture-to-mining ratio) decreased sequentially. Specifically, the fracture-to-mining ratio of hard overburden was 1.59 times that of moderately hard overburden and 1.77 times that of soft overburden, while the fracture-to-mining ratio of moderately hard overburden was 1.11 times that of soft overburden. The prediction results indicate that the prediction results of the northern, middle, and southern belts calculated using the convolutional neural network yielded root mean square errors (RMSEs) of 6.62, 2.20, and 2.60, respectively, while those calculated using Bayes’ formula yielded RMSEs of 21.84, 8.09, and 6.12, respectively. These values were much less than those derived using the empirical formulas for coal mining under buildings, water bodies, and railways (45.91, 13.40, and 21.99, respectively). This suggests that the convolutional neural network and Bayes’ formula outperform the empirical formulas. Notably, the prediction results obtained using the convolutional neural network are closer to the measured results, suggesting the high suitability of the convolutional neural network. This study can provide a basis for predicting the heights of water-conducting fracture zones under different overburden types in the North China-type coalfields.