Yaqiang Gong1,2, Guangli Guo1,2,*
CMES-Computer Modeling in Engineering & Sciences, Vol.119, No.2, pp. 395-408, 2019, DOI:10.32604/cmes.2019.03686
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 More >
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