Coupling Analysis of Seepage Stability in Tailings Pond Based on 3D Numerical Simulation and Machine Learning

Tailings pond is an indispensable component of mining activities, but it also brings significant environmental and safety risks. The infiltration line is the lifeline of tailings ponds, and accurately predicting the infiltration line is still an important issue that is difficult to solve at present. To prevent tailings dam collapse accidents and to fully exploit the spatial characteristics and temporal information provided by the infiltration line data, this article explores the coupled analysis of tailings dam seepage stability based on 3D numerical simulation and machine learning. On this basis, this article designs a high-precision convolutional neural network long short-term memory network optimization model suitable for predicting the infiltration line of tailings ponds. The model uses the convolutional neural network to extract the connections between the infiltration line data in high-dimensional space, and it captures the spatial correlation characteristics of the infiltration line's temporal characteristics.