Research on Rapid Diagnosis Method of Landslide’s Uniform Deformation Stage

WANG Pengwei; AN Yuke

doi:10.12401/j.nwg.2022034

2023 Vol. 56, No. 5

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WANG Pengwei, AN Yuke. 2023. Research on Rapid Diagnosis Method of Landslide’s Uniform Deformation Stage. Northwestern Geology, 56(5): 197-203. doi: 10.12401/j.nwg.2022034

Citation:

WANG Pengwei, AN Yuke. 2023. Research on Rapid Diagnosis Method of Landslide’s Uniform Deformation Stage. Northwestern Geology, 56(5): 197-203. doi: 10.12401/j.nwg.2022034

Research on Rapid Diagnosis Method of Landslide’s Uniform Deformation Stage

WANG Pengwei,
AN Yuke^,

Gansu Province Transportation Planning, Survey & Design Institute Co., Ltd., Lanzhou 730030, Gansu, China

More Information

Corresponding author: AN Yuke, 295419830@qq.com

Received Date: 25 March 2022

Revised Date: 22 August 2022

Accepted Date: 24 October 2022

Publish Date: 20 October 2023

Abstract

Abstract

The improved tangent angle early warning model is an effective landslide early warning method, but the mechanism of the improved tangent angle model is based on the accurate identification of the uniform deformation stage and its rate. The uniform deformation rate of the landslide fluctuates with the change of the disaster prone environment and the landslide evolution stage. Therefore, the tangent angles obtained by the same model under different uniform deformation rates are quite different, which is unfavorable for landslide early warning. Based on this, the paper proposes to use curve convex concave characteristics and filtering technology to adaptively obtain the landslide uniform deformation stage and average rate, and establish rate reliability evaluation rules, so as to achieve real–time correction of the improved tangent angle early warning model and ensure the accuracy of landslide early warning. The research of this paper provides technical support for intelligent and automatic real–time processing of monitoring and early warning data of landslide monitoring and early warning.
- landslides /
- monitoring and warning /
- tangent angle model /
- uniform deformation

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References

[1]	何朝阳. 滑坡实时监测预警系统关键技术及其应用研究[D]. 成都: 成都理工大学, 2020 Google Scholar HE Chaoyang. Research on Key Technology and Application of Real-time Monitoring and Early Warning System of Landslide[D]. Chengdu: Chengdu University of Technology, 2020. Google Scholar
[2]	黄煜, 谢婉丽, 刘琦琦, 等. 基于GIS与MaxEnt模型的滑坡易发性评价——以铜川市中部城区为例[J]. 西北地质, 2023, 56(1): 266−275. Google Scholar HUANG Yu, XIE Wanli, LIU Qiqi, et al. Landslide Susceptibility Assessment Based on GIS and MaxEnt Model: Example from Central Districts in Tongchuan City[J]. Northwestern Geology, 2023,56(1): 266−275. Google Scholar
[3]	李泉, 贾如磊, 李金明, 等. 基于最小二乘法位移传感器数据的曲线拟合[J]. 兰州石化职业技术学院学报, 2012, 12(1): 24-26 Google Scholar LI Quan, JIA Rulei, LI Jinming, et al. Fitting Displacement Sensor Curve Based on Least Squares Method[J]. Lanzhou Petrochemical College of Vocational Technology Journal, 2012, 12(1): 24-26. Google Scholar
[4]	龙悦, 徐光黎, 高幼龙, 等. 数据预处理在滑坡位移相关分析中的应用[J]. 地质科技情报, 2012, 31(2): 122-127 Google Scholar LONG Yue, XU Guangli, GAO Youlong, et al. Application of the Data Preprocessing Methods to the Correlation Analysis of Landslide Displacement[J]. Geological Science and Technology Information, 2012, 31(2): 122-127. Google Scholar
[5]	罗文强, 李飞翱, 刘小珊, 等. 多元时间序列分析的滑坡演化阶段划分[J]. 地球科学, 2016, 41(4): 711-717 Google Scholar LUO Wenqiang, LI Feiao, LIU Xiaoshan, et al. Evolution Stage Division of Landslide Based on Analysis of Multivariate Time Serie[J]. Earth Science, 2016, 41(4): 711-717. Google Scholar
[6]	马海涛, 张亦海, 于正兴. 滑坡速度倒数法预测模型加速开始点识别及临滑时间预测研究[J]. 岩石力学与工程学报. 2021, 40(2): 355-364 Google Scholar MA Haitao, ZHANG Yihai, YU Zhengxing. Research on the identification of acceleration starting point in inverse velocity method and the prediction of sliding time[J]. Chinese Journal of Rock Mechanics and Engineering, 2021, 40(2): 355-364. Google Scholar
[7]	马娟, 赵文炜, 齐干, 等. 基于普适型监测的多参数预警研究-以三峡库区卡门子湾滑坡为例[J]. 西北地质, 2021, 54(3): 259-269 Google Scholar MA Juan, ZHAO Wenyi, QI Gan, et al. Study on the Multi-parameter Early Warning Based on Universal Equipment: A Case of Kamenziwan Landslide in the Three Gorges Reservoir[J]. Northwestern Geology, 2021, 54(3): 259-269. Google Scholar
[8]	孟晓捷, 张新社, 曾庆铭, 等. 基于加权信息量法的黄土滑坡易发性评价——以1: 5万天水市麦积幅为例[J]. 西北地质, 2022, 55(2): 249−259. Google Scholar MENG Xiaojie, ZHANG Xinshe, ZENG Qingming, et al. The Susceptibility Evaluation of Loess Landslide Based on Weighted Information Value Method: Taking 1: 50 000 Map of Maiji District of Tianshui City As an Example[J]. Northwestern Geology, 2022, 55(2): 249-259. Google Scholar
[9]	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 Google Scholar 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. Google Scholar
[10]	王朋伟. 库水作用下滑坡变形演化规律研究[D]. 武汉: 中国地质大学, 2012 Google Scholar WANG Pengwei. Study on Deformation Regularity of Landslide Under The Influence of Reservoir Water[D]. Wuhan: China University of Geosciences, 2012. Google Scholar
[11]	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[J]. 地质通报, 2009, 28(4): 501-505. Google Scholar 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. Google Scholar
[12]	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. Google Scholar 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. Google Scholar
[13]	许强, 汤明高, 黄润秋, 等.大型滑坡监测预警与应急处置[M].北京: 科学出版社, 2020b. Google Scholar XU Qiang, TANG Minggao, HUANG Runqiu, et al. Large-scale Landslide Monitoring and Early Warning and Emergency Response [M]. Beijing: Science Press, 2020b. Google Scholar
[14]	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):1104－1112. Google Scholar 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): 1104-1112. Google Scholar
[15]	薛强, 张茂省. 延安淹土安滑坡监测预警及变形特征[J]. 西北地质, 2018, 51(2): 220-226 Google Scholar XUE Qiang, ZHANG Maosheng. Monitoring Early Warning and Deformation Characteristics of Yantu'an Landslide in Yan'an[J]. Northwestern Geology, 2018, 51(2): 220-226. Google Scholar
[16]	易庆林, 胡大儒, 代天凡, 等. 基于小波分析的滑坡变形规律研究[J]. 南水北调与水利科技. 2013, 11(5): 91-94, 102 Google Scholar YI Qinglin, HU Daru, DAI Tianfan, et al. Deformation Law for a Landslide in the Three Gorges Reservoir Area Based on Wavelet Analysis[J]. South-to-North Water Transfers and Water Science & Technology, 2013, 11(5): 91-94, 102. Google Scholar
[17]	张林梵, 王佳运, 张茂省, 等. 基于BP神经网络的区域滑坡易发性评价[J]. 西北地质, 2022, 55(2): 260−270. Google Scholar ZHANG Linfan, WANG Jiayun, ZHANG Maosheng, et al. Evaluation of Regional Landslide Susceptibility Assessment Based on BP Neural Network[J]. Northwestern Geology, 2022, 55(2): 260−270. Google Scholar
[18]	张林梵.基于时序InSAR的黄土滑坡隐患早期识别——以白鹿塬西南区为例[J]. 西北地质, 2023, 56(3): 250−257. Google Scholar ZHANG Linfan. Early Identification of Hidden Dangers of Loess Landslide Based on Time Series InSAR: A Case Study of Southwest Bailuyuan[J]. Northwestern Geology, 2023, 56(3): 250−257. Google Scholar
[19]	Xuanmei Fan, Qiang Xu, Andres Alonso-Rodriguez, et al. Successive landsliding and damming of the Jinsha Riverin eastern Tibet, China: prime investigation, early warning, and emergencyresponse [J]. Landslides, 2019, 16( 5): 1003-1020. doi: 10.1007/s10346-019-01159-x CrossRef Google Scholar
[20]	Chaoyang He, Nengpan Ju, Qiang Xu, et al. Automated Data Processing and Integration of Large Multiple Data Sources in Geohazards Monitoring[J]. International Journal of Georesources and Environment, 2017, 3(1): 9-21. DOI: 10.15273/ijge.2017.01.003 CrossRef Google Scholar