Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method
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 (INV) has become an effective method to solve this issue. To improve the reliability of landslide...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/16/3/430 |
| _version_ | 1797318206780604416 |
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| author | Yabin Tao Ruixin Zhang Han Du |
| author_facet | Yabin Tao Ruixin Zhang Han Du |
| author_sort | Yabin Tao |
| collection | DOAJ |
| description | 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 (INV) has become an effective method to solve this issue. 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 results show that the sliding process of landslide can be divided into three stages based on the INV: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), and the linear reduction stage (autoregressive stage). The accuracy of the INV 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 the short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. 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. |
| first_indexed | 2024-03-08T03:47:05Z |
| format | Article |
| id | doaj.art-ac9ff4f50fb443e4b63490f53372a6ae |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-08T03:47:05Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-ac9ff4f50fb443e4b63490f53372a6ae2024-02-09T15:24:37ZengMDPI AGWater2073-44412024-01-0116343010.3390/w16030430Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity MethodYabin Tao0Ruixin Zhang1Han Du2School of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaSchool of Energy and Mining Engineering, China University of Mining and Technology (Beijing), Beijing 100083, ChinaState Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, ChinaIn 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 (INV) has become an effective method to solve this issue. 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 results show that the sliding process of landslide can be divided into three stages based on the INV: the initial attenuation stage (regressive stage), the second attenuation stage (progressive stage), and the linear reduction stage (autoregressive stage). The accuracy of the INV 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 the short-term smoothing filter (SSF) and long-term smoothing filter (LSF) in the linear autoregressive stage is better. 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.https://www.mdpi.com/2073-4441/16/3/430failure time of landslideopen-pit coal mineinverse velocityearly warningfield monitoring |
| spellingShingle | Yabin Tao Ruixin Zhang Han Du Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method Water failure time of landslide open-pit coal mine inverse velocity early warning field monitoring |
| title | Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method |
| title_full | Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method |
| title_fullStr | Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method |
| title_full_unstemmed | Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method |
| title_short | Failure Prediction of Open-Pit Mine Landslides Containing Complex Geological Structures Using the Inverse Velocity Method |
| title_sort | failure prediction of open pit mine landslides containing complex geological structures using the inverse velocity method |
| topic | failure time of landslide open-pit coal mine inverse velocity early warning field monitoring |
| url | https://www.mdpi.com/2073-4441/16/3/430 |
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