Abstract: As data collected from the online monitoring system of in-situ leach mining would contain a lot of noise and interference signals due to some influencing factors including temperature difference，frost and disturbance，it is difficult to reach a scheduled data accuracy with the system′s temperature compensation module，resulting in inaccuracy in the follow-up forecasting and pre-warning work.Therefore，after the EMD(Empirical Mode Decomposition) decomposition，the IMF component can achieve freedom refactoring，and remove the high frequency component.It can well purify environment factors on the on-line monitoring displacement data，and the influence of low frequency component can better reflect the actual displacement.The dissertation establishes a forecasting model-EMD-RBFNN（Radical Basis Function neural network）for online monitoring data，levering the best approximation effect of RBFNN，after dealing with the original signal and having the real monitoring data extracted by taking advantage of the adaptive decomposition characteristics of EMD technology.The prediction test is carried out based on the actual measured earth surface displacement data of some rare earth mine.The results show that the surface displacement prediction data boasts more reliability and accuracy as its relative error is within only 0.12%.

Key words: The slope of rare earth mine, In-situ leaching, On-line monitoring, Surface displacement, EMD-RBFNN forecasting model