大数据环境下滑坡宏观位移阶段空间分布规律及预警判据研究  

Research on the Spatial Distribution Law and Early-Warning Criteria of Landslide Displacement Stages in a Big Data Environment

作　　者：王凯 张少杰[2] 马娟[3] 杨红娟[2] 刘敦龙 杨超平 WANG Kai;ZHANG Shaojie;MA Juan;YANG Hongjuan;LIU Dunlong;YANG Chaoping(Institute of Civil Engineering,Zhongyuan University of Technology,Zhengzhou 450007,China;Key Laboratory of Mountain Hazards and Earth Surface Process,Institute of Mountain Hazards and Environment,Chinese Academy of Sciences,Chengdu 610041,China;China Institute of Geo-environment Monitoring,Beijing 100081,China;College of Software Engineering,Chengdu University of Information Technology,Chengdu 610041,China)

机构地区：[1]中原工学院建筑工程学院,河南郑州450007 [2]中国科学院成都山地灾害与环境研究所山地灾害与地表过程重点实验室,四川成都610041 [3]中国地质环境监测院,北京100081 [4]成都信息工程大学软件工程学院,四川成都610041

出　　处：《地球科学进展》2022年第10期1054-1065,共12页Advances in Earth Science

基　　金：国家重点研发计划项目“村寨地质灾害智能监测与治理技术研发及应用示范”(编号:2020YFD1100701);中原工学院青年骨干教师培养计划“基于斜坡单元的区域降雨型滑坡机理预报模型研究”(编号:2020XQG13)资助

摘　　要：准确识别滑坡当前所处变形阶段是滑坡预警的重点问题。许多滑坡位移监测曲线并不存在明显的三阶段特征,难以准确识别滑坡目前所处的阶段。基于此,利用形态学思想突破传统斋藤法的限制,建立能够从形态各异的位移曲线中识别滑坡宏观位移阶段的方法;运用该方法从全国4类岩性大区GNSS地表位移监测数据库中识别出1944条滑坡宏观变形阶段,构建变形阶段大数据样本环境;分析各岩性区内滑坡宏观位移阶段日变形速率分布规律,利用聚类分析构建变形阶段多级预警判据,并对各级预警区间出现的空间概率及持续时间进行讨论。结果表明,4种岩性区内滑坡位移阶段日变形速率均呈现幂函数分布规律,其中日变形速率在10 mm/d以下的数量占比最多;聚类分析表明,各岩性区“无—蓝色—黄色—橙色—红色”五级预警区间内的变形阶段数量均呈现幂函数分布规律。同一预警等级下,岩性1区至岩性4区的变形速率预警阈值呈现递减趋势。变形阶段持续时间分析表明,日速率处于0.08~2.14 mm/d范围的变形阶段持续时间最长,为120.22~160.96 d;日速率处于2734.18~31770.00 mm/d范围的变形阶段持续时间最短,为0.0043~0.0200 d。分析了大数据环境下我国4类岩性区滑坡宏观位移阶段空间分布规律及预警判据,为今后智能型滑坡位移预警模型构建提供科学依据和指导作用。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 below10 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 shortestlasting daily rate was 2734.18~31770.00 mm/d with a duration of 0.0043~0.0200 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.

关 键 词：变形阶段 形态学 位移监测曲线 日变形速率 预警阈值 

分 类 号：P642.22[天文地球—工程地质学]