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Abstract
Big data are widely used in various fields. However, the application of big data is rare in the study of predicting surface subsidence caused by underground mining. Traditional research has the problem of oversimplifying geological mining conditions. 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 CAD software (Rhinoceros) and borehole logs. The data-intensive FLAC3D model was developed using Rhinoceros software to visualize borehole logs in three dimensions. In the three dimensional modeling process, based on the characteristics of the FLAC3D model and borehole logs, 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 70 observation points on the surface. This study shows that the proposed method has obvious advantages over the traditional prediction results. Compared with the traditional prediction results, the relative error of the surface maximum 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 proposed method is the first method to geospatial big data into the mining subsidence prediction research, which is of great significance for improving the accuracy of mining subsidence predictions.
Highlights
1.1 Geospatial big data and mining subsidence predictionGovernments, scientific institutions, commercial companies, and public media all attach great importance to the value of big data, and there is no doubt that the era of big data has come [Yuki (2011); Lohr (2012); Chen, Mao and Liu (2014)]
Undulating stratigraphic interfaces are simplified to horizontal stratigraphic interfaces and the rock layers whose lithology changes with space are simplified into rock layers whose lithology remains unchanged on the horizontal surface [Ma, Yin, Li et al (2017); Pongpanya, Sasaoka, Shimada et al (2017); Ma, Li and Zhang (2017); Cheng, Zhao and Li (2018); Li, Zhao, Guo et al (2018)]
The problem of oversimplification of rock masses exists in the traditional method of using FLAC to predict surface subsidence
Summary
Governments, scientific institutions, commercial companies, and public media all attach great importance to the value of big data, and there is no doubt that the era of big data has come [Yuki (2011); Lohr (2012); Chen, Mao and Liu (2014)]. Undulating stratigraphic interfaces are simplified to horizontal stratigraphic interfaces and the rock layers whose lithology changes with space are simplified into rock layers whose lithology remains unchanged on the horizontal surface [Ma, Yin, Li et al (2017); Pongpanya, Sasaoka, Shimada et al (2017); Ma, Li and Zhang (2017); Cheng, Zhao and Li (2018); Li, Zhao, Guo et al (2018)]. This type of simplification reduces the difficulty of MSP as well as reduces the rationality of the prediction. Inspired by the idea of geospatial big data, MSP requires the use of complete geospatial big data rather than simplified partial data
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