Normality Test for Influence Function of Mining Surface Subsidence Based on Network Observation Station

Abstract

Coal is the primary energy source in China, while underground mining is the mainstream way of coal mining. By triggering surface movement and deformation, underground mining can cause damage to arable land, buildings, roads, and so on, which is detrimental to the ecological environment in the mining area. In order to assess the severity of damage caused by this to the ground buildings and ecological environment in the mining area, it is necessary to predict mining-induced surface subsidence before the mining activities are carried out. Currently, the most used prediction method is the Probability Integral Method. It is based on probabilistic theory to mathematically demonstrate that the surface downwelling caused by underground extraction conforms to normal distribution. However, there is a lack of validation with measured subsidence basin data. Since the 1960s, China has been paying increased attention to the study of mining subsidence. However, there are still few network stations built across China to monitor subsidence basin. Herein, SPSS software is applied to re-analyze these valuable historical data. By analyzing the observation results and comparing them with the calculation results as obtained by using the probability integral method, it can be found out that the surface subsidence in the center of the subsidence basin conforms to normal distribution, not the subsidence at the edge of the subsidence basin. Therefore, it is inevitable for errors to occur at the edge of the subsidence basin when the normal distribution function is used as the mining influence function to calculate the surface subsidence. This conclusion is expected to provide practical reference for the prediction of surface subsidence in coal mines, and this experience can be extended to the mining of other solid minerals.