Failure Prediction of Open-pit Mine Landslide Containing Complex Geological Structure using Inverse Velocity Method: A Case Study in West Open-Pit mine, Pingzhuang, China

In the field of open-pit geological risk management, landslide failure time prediction is one of the important topics. Based on the analysis of displacement monitoring data, the inverse velocity method (IVM) has become an effective method to solve this issue. In order to improve the reliability of landslide prediction, four filters were used to test the velocity time series, and the effect of landslide failure time prediction was compared and analyzed. The IVM is used to predict the failure time of open-pit coal mine landslide. The results show that the sliding process of landslide can be divided into three stages based on the IVM: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), the linear reduction stage (autoregressive stage). The accuracy of the IVM is closely related to the measured noise of the monitoring equipment and the natural noise of the environment, which will affect the identification of different deformation stages. Compared with the raw data and the exponential smoothing filter (ESF) models, the fitting effect of short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. A slope displacement pixel difference method based on fitting accuracy and field monitoring signals is proposed to determine the point onset-of-acceleration (OOA) that is very important role for landslide prediction. A stratified prediction method combining SSF and LSF is proposed. The prediction method is divided into two levels, and the application of this method is given.

Movements, failure and climatic control of the Veslemannen rockslide, Western Norway

On September 5, 2019, the Veslemannen unstable rock slope (54,000 m3) in Romsdalen, Western Norway, failed catastrophically after 5 years of continuous monitoring. During this period, the rock slope weakened while the precursor movements increased progressively, in particular from 2017. Measured displacement prior to the failure was around 19 m in the upper parts of the instability and 4–5 m in the toe area. The pre-failure movements were usually associated with precipitation events, where peak velocities occurred 2–12 h after maximum precipitation. This indicates that the pore-water pressure in the sliding zones had a large influence on the slope stability. The sensitivity to rainfall increased greatly from spring to autumn suggesting a thermal control on the pore-water pressure. Transient modelling of temperatures suggests near permafrost conditions, and deep seasonal frost was certainly present. We propose that a frozen surface layer prevented water percolation to the sliding zone during spring snowmelt and early summer rainfalls. A transition from possible permafrost to a seasonal frost setting of the landslide body after 2000 was modelled, which may have affected the slope stability. Repeated rapid accelerations during late summers and autumns caused a total of 16 events of the red (high) hazard level and evacuation of the hazard zone. Threshold values for velocity were used in the risk management when increasing or decreasing hazard levels. The inverse velocity method was initially of little value. However, in the final phase before the failure, the inverse velocity method was useful for forecasting the time of failure. Risk communication was important for maintaining public trust in early-warning systems, and especially critical is the communication of the difference between issuing the red hazard level and predicting a landslide.

SLO VS INVERSE VELOCITY METHOD: A STUDY TO EXAMINE EFFECTIVE APPROACH TO PREDICT SLOPE FAILURE

ABSTRACT Provided with accurate and quasi real time deformation data, there are at least 2 methods that can be utilized to predict a slope failure. Inverse velocity method, coined by Fukuzono, aims at the interception of inverse velocity line to zero value at X time axis as the prediction of slope failure. More recent method called SLO, develop by Mufundirwa, puts emphasize on interception of acceleration regression line with X velocity axis. This paper is intended first and foremost to establish well-structured comparison between the two aforementioned methods. By using the same set of displacement data that show progressive deformation trend from Slope Stability radar, both SLO & Inverse Velocity method will be put into trial. Not only the accuracy of the failure prediction time, but also the comparison between the R2 attribute will be examine to reveal which method that yield better data statistically. One of the selected study case, from several which is presented on the paper, reveal that SLO method give failure prediction closer with the actual failure compared to Inverse Velocity method. The actual failure is happening at 21:59 AM January 1st 2016. SLO method generates failure prediction 10 minutes prior the actual failure, while Inverse Velocity generates failure prediction plus 68 minutes after the failure. R2 value for SLO method and Inverse Velocity method respectively are 0.710 & 0.630. Apart from this results comparison, a more in depth examination toward the nature of both methods delivers pro & con of each method. SLO method seems more accurate but having a constraint in which if there are no previous database of maximum velocity during collapse, prediction is almost impossible to make. Inverse Velocity method could address this flaw by projecting the inverse velocity line to zero value for the very least. Further explanation about the flaw and advantages of both methods will be conveyed in more detail on the later part of this paper. Key words: Failure Prediction, SLO, Inverse Velocity, SSR ABSTRAK Dengan adanya pengambilan data deformasi yang akurat dan mendekati “real time”, terdapat setidaknya dua metode yang dapat digunakan untuk memprediksi longsor. Metode inverse velocity, yang dikembangkan oleh Fukuzono, adalah metode yang menggunakan perpotongan grafik inverse velocity dengan titik nol sebagai acuan atau nilai dari prediksi longsor. Metode lain yang lebih baru dibandingkan metode inverse velocity adalah metode SLO yang dikembangkan oleh Mufundirwa. Metode ini lebih ditekankan pada perpotongan antara grafik akselerasi dengan nilai kecepatan pada sumbu X. Tujuan utama dari paper ini adalah penyajian perbandingan yang terstruktur antara kedua metode tersebut. Penelitian terhadap metode SLO dan inverse velocity menggunakan data deformasi progresif yang sama dari Slope Stability Radar. Tidak hanya keakuratan prediksi waktu longsor, tetapi perbandingan nilai R2 pun akan menentukan metode yang lebih efektif secara statistik. Pada salah satu studi kasus, dari beberapa kasus yang dibahas di paper ini, menunjukkan bahwa metode SLO memberikan prediksi waktu longsor yang lebih mendekati waktu longsor yang sebenarnya jika dibandingkan dengan metode inverse velocity. Longsor yang sebenarnya terjadi pada tanggal 1 Januari 2016, pukul 21:59. Metode SLO menghasilkan prediksi longsor 10 menit lebih awal dari waktu longsor yang sebenarnya, dimana metode inverse menghasilkan prediksi longsor 68 menit setelah waktu longsor. Nilai R2 untuk metode SLO dan inverse velocity adalah 0.71 dan 0.63. Di samping perbandingan kedua hasil di atas, pemahaman lebih mendalam tentang sumber dari kedua metode tersebut memunculkan hasil plus dan minus dari masing-masing metode. Metode SLO memang terlihat lebih akurat namun metode ini membutuhkan data kecepatan maksimal saat kejadian longsor sebelumnya. Jika tidak ada, maka prediksi hampir tidak mungkin untuk dibuat. Sebaliknya, kelemahan tersebut tidak terdapat pada metode inverse velocity karena dapat diproyeksikan pada titik nol. Penjelasan lebih dalam mengenai kelebihan dan kekurangan dari kedua metode tersebut akan dibahas selanjutnya pada paper ini. Kata kunci: Prediksi longsor, SLO, Inverse velocity, SSR