Investigation of failure prediction of open-pit coal mine landslides containing complex geological structures using the inverse velocity method

2022 ◽  
Author(s):  
Han Du ◽  
Danqing Song
2021 ◽  
Author(s):  
Han Du ◽  
Danqing Song

Abstract 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.


2020 ◽  
Vol 1 (1) ◽  
pp. 525-532
Author(s):  
Maria Christine Rosaria ◽  
Rania Salsabila ◽  
Muhammad Khalif Arda ◽  
Fery Andika Cahyo ◽  
Rachmat Hamid Musa

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


2012 ◽  
Vol 599 ◽  
pp. 272-277 ◽  
Author(s):  
Zhi Bin Liu ◽  
Xiao Wei Yang

This paper used RBF artificial neural network to evaluate the underground water contaminated by the leachate of waste dump of open pit coal mine of Xinqiu in Fuxin. Firstly, with the advantages of neural network method in dealing with nonlinear problem, the RBF neural network model was built. Then, the normalized standard matrix was taken as training sample and the MATLAB software was used to train the training sample. Finally, the monitoring data were taken as test samples and were inputted in the RBF neural network model to evaluate the groundwater quality of study area. At the same time, the concept of degree of membership was adopted in the result making it more objective and accurate. The result shows that the ground water of this mining is seriously polluted, class of its pollution is Ⅳ-Ⅴ.The method with strong classification function and reliable evaluation results is simple and effective, and can be widely applied in all kinds of water resources comprehensive evaluation.


Author(s):  
Jiachen Wang ◽  
Wenhui Tan ◽  
Shiwei Feng ◽  
Rudi Zhou

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