Research on the Spatial Distribution Law and Early-Warning Criteria of Landslide Displacement Stages in a Big Data Environment
Received date: 2022-03-31
Revised date: 2022-06-12
Online published: 2022-10-18
Supported by
the National Key Research and Development Program of China “Research and application of intelligent monitoring and treatment technology for geohazards in villages”(2020YFD1100701);The Young Scholar Training Program of Zhongyuan University of Technology “Physical prediction model of regional scale landslides based slope unit”(2020XQG13)
Identification of the landslide deformation stage is a key aspect of landslide warning systems. However, many displacement curves lack the obvious characteristics of the three stages, making it challenging to identify the deformation stages of landslides. To overcome the defects of the Saito method, we proposed a method to extract the deformation stages based on a morphology analysis. A total of 1944 deformation stages were identified from the GNSS surface-displacement monitoring database to form a big data environment. Then, we analyzed the spatial distribution of the displacement stages of various lithologic zones in China and used cluster analysis to develop multi-stage warning criteria for deformation stages. The spatial probability and duration of each warning level were also discussed in this study. Analysis results indicated that the power law adequately described the distribution of daily deformation rate for each lithologic region, with daily deformation rates below 10 mm/d accounting for the majority of observations. Cluster analysis revealed that the number of deformation stages within the “none”, “blue”, “yellow”, “orange”, and “red” warning levels also followed the power rule for each lithologic region. It should be noted that the warning threshold of the deformation rate decreased from lithology 1 to lithology 4 at the same warning level. According to the duration analysis of the deformation stage, the longest-lasting daily rate was 0.30~2.14 mm/d with a duration of 120.22~160.96 days; whereas the shortest-lasting daily rate was 2 734.18~31 770.00 mm/d with a duration of 0.004 3~0.020 0 days. In this study, we analyzed the spatial distribution and early-warning criteria of landscape development stages in a big data environment, which could provide a scientific foundation and direction for the development of an intelligent landslide warning model.
Kai WANG , Shaojie ZHANG , Juan MA , Hongjuan YANG , Dunlong LIU , Chaoping YANG . Research on the Spatial Distribution Law and Early-Warning Criteria of Landslide Displacement Stages in a Big Data Environment[J]. Advances in Earth Science, 2022 , 37(10) : 1054 -1065 . DOI: 10.11867/j.issn.1001-8166.2022.042
1 | SAITO M. Research on forecasting the time of occurrence of slope failure[J]. Railway Technical Research Institute, Quarterly Reports, 1969, 17(2):29-38. |
2 | HE Keqiang, GUO Dong, ZHANG Peng, et al. The direction ratio of vertical displacement for rainfall-induced landslides and its early warning criterion[J]. Rock and Soil Mechanics, 2017, 38(12): 3 649-3 659, 3 669. |
2 | 贺可强, 郭栋, 张朋, 等. 降雨型滑坡垂直位移方向率及其位移监测预警判据研究[J]. 岩土力学, 2017, 38(12): 3 649-3 659, 3 669. |
3 | XU Qiang, TANG Minggao, XU Kaixiang, et al. Research on space-time evolution laws and early warning-prediction of landslides[J]. Chinese Journal of Rock Mechanics and Engineering, 2008, 27(6): 1 104-1 112. |
3 | 许强, 汤明高, 徐开祥, 等. 滑坡时空演化规律及预警预报研究[J]. 岩石力学与工程学报, 2008, 27(6): 1 104-1 112. |
4 | ZHAO Yonghong, WANG Hang, ZHANG Qiong, et al. Overview of landslide displacement monitoring methods[J]. Progress in Geophysics, 2018, 33(6): 2 606-2 612. |
4 | 赵永红, 王航, 张琼, 等. 滑坡位移监测方法综述[J]. 地球物理学进展, 2018, 33(6): 2 606-2 612. |
5 | XU Q, YUAN Y, ZENG Y P, et al. Some new pre-warning criteria for creep slope failure[J]. Science China Technological Sciences, 2011, 54(1): 210-220. |
6 | XUE L, QIN S Q, LI P, et al. New quantitative displacement criteria for slope deformation process: from the onset of the accelerating creep to brittle rupture and final failure[J]. Engineering Geology, 2014, 182: 79-87. |
7 | YANG B B, YIN K L, LACASSE S, et al. Time series analysis and long short-term memory neural network to predict landslide displacement[J]. Landslides, 2019, 16(4): 677-694. |
8 | INTRIERI E, CARLà T, GIGLI G. Forecasting the time of failure of landslides at slope-scale: a literature review[J]. Earth-Science Reviews, 2019, 193: 333-349. |
9 | MIAO F S, WU Y P, XIE Y H, et al. Prediction of landslide displacement with step-like behavior based on multialgorithm optimization and a support vector regression model[J]. Landslides, 2018, 15(3): 475-488. |
10 | XU Q, PENG D L, ZHANG S, et al. Successful implementations of a real-time and intelligent early warning system for loess landslides on the Heifangtai Terrace, China[J]. Engineering Geology, 2020, 278: 105817. |
11 | CHAE B G, PARK H J, CATANI F, et al. Landslide prediction, monitoring and early warning: a concise review of state-of-the-art[J]. Geosciences Journal, 2017, 21(6): 1 033-1 070. |
12 | BAI Jie, JU Nengpan, ZHANG Chengqiang, et al. Characteristics and successful early warning case of Xingyi landslide in Guizhou Province[J]. Journal of Engineering Geology, 2020, 28(6): 1 246-1 258. |
12 | 白洁, 巨能攀, 张成强, 等. 贵州兴义滑坡特征及过程预警研究[J]. 工程地质学报, 2020, 28(6): 1 246-1 258. |
13 | XU Qiang, ZENG Yuping, QIAN Jiangpeng, et al. Study on a improved tangential angle and the corresponding landslide pre-warning criteria[J]. Geological Bulletin of China, 2009, 28(4): 501-505. |
13 | 许强, 曾裕平, 钱江澎, 等. 一种改进的切线角及对应的滑坡预警判据[J]. 地质通报, 2009, 28(4): 501-505. |
14 | QI Xing, ZHU Xing, XU Qiang, et al. Improvement and application of landslide proximity time prediction method based on saito model[J]. Journal of Engineering Geology, 2020, 28(4): 832-839. |
14 | 亓星, 朱星, 许强, 等. 基于斋藤模型的滑坡临滑时间预报方法改进及应用[J]. 工程地质学报, 2020, 28(4): 832-839. |
15 | WANG Yifan, QI Xing, CHENG Qian, et al. Tangent angle early warning-based method for data processing of landslide uniform deformation rate[J]. Water Resources and Hydropower Engineering, 2021, 52(12): 185-190. |
15 | 王一帆, 亓星, 程倩, 等. 基于切线角预警的滑坡匀速变形速率数据处理方法[J]. 水利水电技术, 2021, 52(12): 185-190. |
16 | WANG Dongyue. Research on key technologies of landslide early warning based on high precision measurement[D]. Beijing: Beijing University of Technology, 2020. |
16 | 王东岳. 基于高精度测量的山体滑坡预警关键技术研究[D]. 北京: 北京工业大学, 2020. |
17 | LIU Chuanzheng. Three types of displacement-time curves and early warning of landslides[J]. Journal of Engineering Geology, 2021, 29(1): 86-95. |
17 | 刘传正. 累积变形曲线类型与滑坡预测预报[J]. 工程地质学报, 2021, 29(1): 86-95. |
18 | HUANG Xiaohu, LEI Dexin, XIA Junbao, et al. Forecast analysis and application of stepwise deformation of landslide induced by rainfall[J]. Rock and Soil Mechanics, 2019, 40(9): 3 585-3 592, 3 602. |
18 | 黄晓虎, 雷德鑫, 夏俊宝, 等. 降雨诱发滑坡阶跃型变形的预测分析及应用[J]. 岩土力学, 2019, 40(9): 3 585-3 592, 3 602. |
19 | LI Cong, ZHU Jiebing, WANG Bin, et al. Critical deformation velocity of landslides in different deformation phases[J]. Chinese Journal of Rock Mechanics and Engineering, 2016, 35(7): 1 407-1 414. |
19 | 李聪, 朱杰兵, 汪斌, 等. 滑坡不同变形阶段演化规律与变形速率预警判据研究[J]. 岩石力学与工程学报, 2016, 35(7): 1 407-1 414. |
20 | XU Qiang, PENG Dalei, HE Chaoyang, et al. Theory and method of monitoring and early warning for sudden loess landslide—a case study at Heifangtai Terrace[J]. Journal of Engineering Geology, 2020, 28(1): 111-121. |
20 | 许强, 彭大雷, 何朝阳, 等. 突发型黄土滑坡监测预警理论方法研究: 以甘肃黑方台为例[J]. 工程地质学报, 2020, 28(1): 111-121. |
21 | WANG Nianqin, SHEN Huihui, LU Xingsheng. Development status and problem countermeasures of slope deformation monitoring technology[J]. Science Technology and Engineering, 2021, 21(19): 7 845-7 855. |
21 | 王念秦, 申辉辉, 鲁兴生. 边坡变形监测技术发展现状及问题对策[J]. 科学技术与工程, 2021, 21(19): 7 845-7 855. |
22 | XU Qiang. Understanding the landslide monitoring and early warning: consideration to practical issues[J]. Journal of Engineering Geology, 2020, 28(2): 360-374. |
22 | 许强. 对滑坡监测预警相关问题的认识与思考[J]. 工程地质学报, 2020, 28(2): 360-374. |
23 | LU Ke, YU Bin, HAN Lin, et al. A study of the relationship between frequency of debris flow and the lithology in the catchment of debris flow[J]. Advances in Earth Science, 2011, 26(9): 980-990. |
23 | 鲁科, 余斌, 韩林, 等. 泥石流流域岩性的坚固系数与暴发频率的关系[J]. 地球科学进展, 2011, 26(9): 980-990. |
24 | YIN Y P, WANG H D, GAO Y L, et al. Real-time monitoring and early warning of landslides at relocated Wushan Town, the Three Gorges Reservoir, China[J]. Landslides, 2010, 7(3): 339-349. |
25 | JU N P, HUANG J, HUANG R Q, et al. A real-time monitoring and early warning system for landslides in Southwest China[J]. Journal of Mountain Science, 2015, 12(5): 1 219-1 228. |
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