基于地下水动力特征监测的岩溶塌陷预警阈值探索——以广州金沙洲岩溶塌陷为例

蒙 彦; 黄健民; 贾 龙

doi:10.11932/karst20180311

基于地下水动力特征监测的岩溶塌陷预警阈值探索——以广州金沙洲岩溶塌陷为例

doi: 10.11932/karst20180311

1.
中国地质大学（武汉）/中国地质科学院岩溶地质研究所/自然资源部、广西岩溶动力学重点实验室
2.
广州市地质调查院
3.
中国地质科学院岩溶地质研究所/自然资源部、广西岩溶动力学重点实验室

基金项目: 中国地质调查项目（1212011220192，DD20160254）”、国家自然科学基金项目（41302255，41402284）共同支助

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出版历程
- 发布日期: 2018-06-25

Early warning threshold of sinkhole collapse based on dynamic characteristics from groundwater monitoring: A case study of Jinshazhou of Guangzhou, China

1.
School of environmental studies, China University of Geosciences/Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR
2.
Institute of Guangzhou Geological Survey
3.
Institute of Karst Geology, CAGS/Key Laboratory of Karst Dynamics, MNR & GZAR

摘要

摘要: 监测预警是岩溶塌陷地质灾害防治领域急需解决的技术难题。文章在总结当前岩溶塌陷监测预警研究现状的基础上，以广州金沙洲地区岩溶塌陷监测为例，通过对不同岩溶塌陷阶段地下水动力条件监测数据分析，发现数据异常值反映了不同工况地下水动态和岩溶塌陷发展阶段的突变关系，以此为基础，通过曲线拟合和残差分析确定了岩溶塌陷预警的置信带。通过对比分析异常值出现最多次数时间，最大及最小异常值出现时间和实际岩溶塌陷发生时间，验证了运用异常数据分析法进行岩溶塌陷预警是可行的，监测数据间隔越密，捕获的异常值越多，预警越准确。
- 岩溶塌陷 /
- 监测预警 /
- 残差分析 /
- 置信带 /
- 数据突变
Abstract: Karst sinkhole collapse early warning based on monitoring data has long been a prominent technical problem which needs to be solved in the field of geohazard. This paper analyses the current status of sinkhole collapse monitoring and early warning research, and then presents a case study of Jinshazhou of Guangzhou on this issue. The analytical result of groundwater monitoring data shows that the data anomalies largely reflect the abrupt change of groundwater dynamic and the stage of relevant karst collapse development. On this basis, through curve fitting and residual analysis this work determines the confidence belt of sinkhole collapse early warning. Comparing the times of most frequent anomalies, occurrence of maximum and minimum outliers, and the actual times of karst collapse, it is confirmed that it is feasible to use the anomaly data analysis method to make early warning of sinkhole collapse. With shorter monitoring data intervals, capturing more anomalies, the early warning will be more accurate.
- karst sinkhole collapse /
- monitoring and early-warning /
- residual analysis /
- confidence band /
- data mutation

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