Development of a Filtered Inverse Velocity Method Analyzer: A Comparative Study of Smoothing Filters in Surface Mines for optimization of slope failure predictions
Development of a Filtered Inverse Velocity Method Analyzer
DOI:
https://doi.org/10.17159/Abstract
The Inverse Velocity Method (IVM) has proven to be an effective approach for predicting slope failures in surface mines by analyzing displacement monitoring data. However, the accuracy of IVM 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 (ESF), Short-Term Smoothing Filter (SSF), Long-Term Smoothing Filter (LSF) and also compares it to raw data (no filtering). A refined prediction framework (Filtered Inverse Velocity Method Analyzer) is proposed to improve slope failure forecasting in surface mining operations. The results demonstrate that filter selection plays a crucial role in optimizing failure time predictions, offering valuable insights for geotechnical monitoring and early warning systems in surface mines.
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Copyright (c) 2026 Mohd Maneeb Masood
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