A data-intensive FLAC3D computation model: Application of geospatial big data to predict mining induced subsidence

Abstract

Although big data are widely used in various fields, its application is still rare in the study of mining subsidence prediction (MSP) caused by underground mining. Traditional research in MSP has the problem of oversimplifying geological mining conditions, ignoring the fluctuation of rock layers with space. In the context of geospatial big data, a data-intensive FLAC3D (Fast Lagrangian Analysis of a Continua in 3 Dimensions) model is proposed in this paper based on borehole logs. In the modeling process, we developed a method to handle geospatial big data and were able to make full use of borehole logs. The effectiveness of the proposed method was verified by comparing the results of the traditional method, proposed method, and field observation. The findings show that the proposed method has obvious advantages over the traditional prediction results. The relative error of the maximum surface subsidence predicted by the proposed method decreased by 93.7% and the standard deviation of the prediction results (which was 70 points) decreased by 39.4%, on average. The data-intensive modeling method is of great significance for improving the accuracy of mining subsidence predictions.