Development of a filtered inverse velocity method analyser: A comparative study of smoothing filters in surface mines for optimisation of slope failure predictions

The inverse velocity method has proven to be an effective approach for predicting slope failures in surface mines by analysing displacement monitoring data. However, the accuracy of inverse velocity method predictions is significantly affected by instrumental noise and natural environmental variations, which influence the identification of different deformation stages. To enhance predictive accuracy, this study applies and evaluates three filtering techniques to velocity time series data: Exponential smoothing filter, short-term smoothing filter, long-term smoothing filter and also compares it to raw data (no filtering). A refined prediction framework, that is, filtered inverse velocity method analyser, is proposed to improve slope failure forecasting in surface mining operations. The results demonstrate that filter selection plays a crucial role in optimising failure time predictions, offering valuable insights for geotechnical monitoring and early warning systems in surface mines.