Mechanism of karst ground collapse and evaluation of disaster risk in Chengnan community, Yingde City, China
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摘要: 岩溶地面塌陷是常见的地质灾害之一，具有隐蔽性、突发性、不均一性等特点。英德城区突发大规模地面塌陷，灾害严重。综合调查和分析表明，区内上覆松散沉积物和下伏岩溶洞穴是集中地面塌陷的物质基础，北江水位波动及地下水抽提引起的潜蚀和真空吸蚀水动力作用，以及污水渗漏引起的化学溶蚀作用是塌陷形成的主要机制。文章选取8个易发性指标和2个易损性指标并结合层次分析法，建立了岩溶地面塌陷灾害风险综合指数模型，对研究区进行了风险评估，结果表明高风险区主要分布在污水处理厂—水泥厂一带，占研究区面积70.83%；中风险区主要分布研究区西北角，占研究区面积29.17%。建议加强地下水动态监测与管理，以及地表污水防渗措施，以防御该区地面塌陷灾害，降低致灾风险。Abstract:
Karst ground collapse is one of the most common geological hazards in karst areas. It is characterized by concealment, sudden occurrence and inhomogeneity, along with various other complex features. Understanding the mechanisms of karst ground collapse and conducting risk assessments for such events are of great significance for urban development and engineering construction. In November 2020, a large-scale karst ground collapse occurred in Chengnan community of Yingde City, Guangdong Province. A total of 31 collapse pits were identified, predominantly exhibiting elliptical and circular shapes, and spatially distributed in three strips. The duration was notably lengthy, resulting in relatively severe disaster impacts. Comprehensive investigation results and analysis show that the karst ground collapse in the study area primarily occurred in the main groundwater runoff zone, which is characterized by relatively thick overlaying layers and significant karst development. The spatial distribution of the strip align with the arrangement of three fault zones in the study area. In the vicinity of a sewage treatment plant, the stacking of karst caves is evident, characterized by a significant cumulative thickness of the caves, high permeability of the rock layers, and loose overlying soil with low cohesion. These factors created fundamental conditions for the concentrated occurrence of ground collapse. In the study area, the triggering mechanisms of karst ground collapse are influenced by frequent fluctuations in the water levels of the nearby Beijiang River, as well as human activities that involve groundwater pumping and draining. These factors induce hydrodynamic effects, including subsurface erosion and vacuum suction erosion of groundwater. Additionally, the presence of sewage leakage near the sewage treatment plant at the center of the study area altered hydrochemical properties of groundwater, resulting in chemical dissolution. Therefore, the primary mechanisms driving karst ground collapse in this area are chemical dissolution and hydrodynamic effects. In order to facilitate the formulation of targeted preventive measures, we selected eight susceptibility indicators and two vulnerability indicators after screening each indicator factor in the study area. These indicator factors were combined with the Analytic Hierarchy Process (AHP) to establish a comprehensive index model of karst ground collapse risk suitable for the study area. By leveraging GIS spatial analysis functions, we conducted a relatively accurate risk assessment of the study area. The results show that the risk of karst ground collapse in the study area can be categorized into two levels: high-risk and moderate-risk. The high-risk zone is mainly distributed around the sewage treatment plant and cement plant area, comprising 70.83 % of the total study area. Conversely, the moderate-risk zone is mainly located in the northwest corner of the study area, accounting for 29.17 % of the total. In conclusion, it is recommended to strengthen the dynamic monitoring and management of groundwater, along with implementing measures to prevent and control surface sewage seepage. These actions are essential to protect the region from ground collapse hazards and to reduce the risk of disasters. -
Key words:
- karst ground collapse /
- formation mechanism /
- susceptibility /
- vulnerability /
- risk assessment
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图 2 钻孔溶洞发育层数（a）和累计高度（b）等值线图
Figure 2. Contour map illustrating the number of layers (a) and cumulative heights (b) of cave development based on borehole data
图 3 2021年钻孔水位和北江水位关系图（a）和北江各月平均水位变化及塌陷统计图（b）
Figure 3. Relationship between borehole water levels and water levels of the Beijiang River in 2021 (a) and changes in monthly average water levels of the Beijiang River and statistics of ground collapse (b)
图 4 厂内和厂外地下水水样Piper三线图（a）和Gibbs图（b、c）
Figure 4. Piper diagram (a) and Gibbs diagram (b and c) of the groundwater samples inside and outside the plant
图 6 研究区岩溶地面塌陷易发性和易损性分区图
Figure 6. Zoning map of susceptibility and vulnerability to karst ground collapses in the study area
图 7 研究区岩溶地面塌陷灾害风险性分区图
Figure 7. Zoning map of risks of karst ground collapses in the study area
表 1 研究区岩溶地面塌陷统计表
Table 1. Statistics of karst ground collapses in the study area
条带 走向 数量/个 直径/m 深度/m 形态 范围 平均值 范围 平均值 Ⅰ NE40° 10 3.0~10.0 5.9 3.0~9.0 3.2 椭圆—近似圆形 Ⅱ NE30° 17 1.5~15.0 5.1 0.8~4.5 2.5 椭圆—圆形 Ⅲ NE30° 4 3.0~24.5 9.1 1.0~11.5 6.5 椭圆—近似圆形 
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表 2 地下水示踪试验结果表
Table 2. Results of groundwater tracer test
投放点 接收点 连通距离/m 初见流速/m·h−1 平均流速/m·h−1 示踪剂浓度曲线形态 推测通道发育特征 ZK252
（钼酸铵）ZK294 141 31.33 8.81 双峰 至少2个通道 ZK188 282.8 1.12 0.98 单峰，峰形较宽 至少1个通道，发育1个溶潭 ZK235-1 106 0.47 0.47 双峰 至少2个通道 ZK289 223.6 17.89 2.65 三峰且拖尾 至少3个通道，发育2个溶潭 ZK373 364 5.09 2.86 1个尖峰，3个纯峰 至少4个通道，一、三通道各发育1个溶
潭，第四通道发育两个溶潭，规模较大ZK352 583 — 2.11 2个钝峰 至少2个通道，各发育1个溶潭 ZK201 180 — 0.49 双峰，峰形较宽，
密集，呈锯齿状至少2个通道，主通道发育1个大溶潭，
支通道发育1个小溶潭ZK298
（荧光素钠）ZK244 335.4 111.8 2.65 单峰拖尾，来回波动 至少1个通道，裂隙发育 ZK352 335.4 — 1.21 单一钝峰拖尾 至少1个通道，1个溶潭，裂隙发育 ZK260 141.4 18.8 1.27 锯齿状波动 主要以裂隙为主 注：“—”表示未检测出。
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表 3 不同取样深度厂内外土样力学指标平均值表
Table 3. Mean values of mechanical indicators of soil samples inside and outside the plant at different sampling depths
深度/m 液性指数IL 压缩系数a/MPa−1 压缩模量Es/MPa 黏聚力c/kPa 内摩擦角φ/° 厂内 厂外 厂内 厂外 厂内 厂外 厂内 厂外 厂内 厂外 ＜5 0.47 0.46 0.45 0.44 4.24 4.24 25.44 25.74 14.25 14.26 5~10 0.43 0.38 0.42 0.39 4.65 4.9 26.91 27.97 14.98 15.92 10~15 0.37 0.3 0.38 0.33 5.16 5.67 29.1 32.6 16.15 17.43 15~20 0.33 0.28 0.35 0.33 5.23 5.5 31.83 33.45 17.22 18.03 ＞20 0.13 0.12 0.23 0.23 7.48 7.35 36.7 38.44 21.5 21.33
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表 4 岩溶地面塌陷易发性评价体系
Table 4. Evaluation system of susceptibility to karst ground collapse
评估指标 分级和取值 条件层 指标因子层 影响程度强 影响程度较强 影响程度中等 影响程度弱 4 3 2 1 A.塌陷密度 塌陷坑密度p1 ＞0.75 0.5~0.75 0.25~0.5 0~0.25 B.地质环
境条件第四系覆盖层厚度p2 ＜10 m 10~20 m 20~30 m ＞30 m 构造断裂p3 强发育 中等发育 弱发育 不发育 岩溶发育
程度p4平均线岩溶率p4-1 ＞0.4 0.2~0.4 0.05~0.2 ＜0.05 −10~0 m标高段线岩溶率p4-2 ＞0.4 0.2~0.4 0.05~0.2 ＜0.05 0~10 m标高段线岩溶率p4-3 ＞0.4 0.2~0.4 0.05~0.2 ＜0.05 溶洞累计高度p-4 ＞20 m 15-20 m 5~15 m ＜5 m 溶洞层数p4-5 6~8层 5~7层 3~4层 0~2层 基岩岩性p5 灰岩 灰岩与炭质灰岩重合区 炭质灰岩 — 富水性p6, 涌水量L·（s·m）−1 ＞5.0 1.0~5.0 0.1~1.0 ＜0.1 C.塌陷诱
发因素地下水径流p7 水力梯度大 水力梯度中等 水力梯度小 — 人类活动p8 强烈 中等 较弱 弱 注：“—”表示无
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表 5 易发性对条件层的判断矩阵
Table 5. Judgment matrix of susceptibility to the conditional layer
易发性-条件层 A B C A 1 1/2 1 B 2 1 1 C 1 1 1
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表 6 指标因子层对地质环境条件的判断矩阵
Table 6. Judgment matrix of indicator factor layers to geological environment conditions
指标因子 p2 p3 p4 p5 p6 p-1 p-2 p-3 p-4 p-5 p2 1 1/3 1/3 2 2 1/3 1/2 1/2 1 p3 3 1 1/2 2 2 2 2 1/2 3 p4 p4-1 3 2 1 4 4 3 3 3 4 p4-2 1/2 1/2 1/4 1 1 2 2 2 3 p4-3 1/2 1/2 1/4 1/2 1 2 2 1 2 p4-4 3 1/2 1/3 1/2 1/2 1 2 1 2 p4-5 2 1/2 1/3 1/2 1/2 1/2 1 1 3 p5 2 2 1/3 1/2 1 1 1 1 1 p6 1 1/3 1/4 1/3 1/2 1/2 1/3 1 1
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表 7 指标因子层对塌陷诱发因素的判断矩阵
Table 7. Judgment matrix of indicator factor layers to collapse-inducing factors
指标因子 p7 p8 p7 1 2 p8 1/2 1
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表 8 评价指标对岩溶地面塌陷易发影响权重表
Table 8. Weighting of the impact of evaluation factors on susceptibility to karst ground collapse
指标因子 A B C $ {w}_{i} $ 排序 0.2599 0.4126 0.3275 p1 1.0000 0.2599 1 p2 0.0777 0.0321 7 p3 0.1444 0.0596 5 p4 0.6290 0.2594 2 p5 0.0996 0.0411 6 p6 0.0493 0.0204 8 p7 0.6667 0.2183 3 p8 0.3333 0.1092 4
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表 9 易损性评价指标分级赋值表
Table 9. Hierarchical assignment of vulnerability evaluation indicators
因子层 赋值 4 3 2 1 土地用地类型q1 城镇建设、工矿建设、村庄建设用地 水利水电、油气管道和交通用地 农业用地 林业用地、未利用地 人口密度 q2 小区、村庄等人口密集区 工厂、工地等人口密集区 林地道路 湖泊水面
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表 10 易损性对指标因子层的判断矩阵
Table 10. Judgment matrix of vulnerability to indicator factor layers
指标因子 q1 q2 $ {w}_{j} $ q1 1 1 0.5 q2 1 1 0.5
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表 11 风险性对P-Q层的判断矩阵
Table 11. Judgment matrix of risks to layers P-Q
指标因子 P Q 权重 P 1 2 0.6667 Q 1/2 1 0.3333
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表 12 岩溶地面塌陷风险性分布主要特征表
Table 12. Main characteristics of the risk distribution of karst ground collapse
风险分区 面积/km2 区号 面积占比/% 塌陷坑/个 易发性 威胁对象 易损性 高风险区
（Ⅰ）425284 Ⅰ 70.8 25 多为高—中易发，西南、东南
局部小面积低易发居民区、污水处理厂、水泥厂、
建筑工地、人员、道路等高 中风险区
(Ⅱ)175130 Ⅱ1 7.8 2 多为中易发，西部局部中—低易发 农田、菜地 中 Ⅱ2 2.3 1 多为高易发，西部局部中易发 农田、菜地 中 Ⅲ3 19.1 3 多为中易发，西北小部分低易发，
东南角为高易发农田、菜地、水塘、林地、道路 中
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[1] 杨元丽, 孟凡涛, 李明惠. 黔中地区浅覆盖型岩溶塌陷成因机制与防治对策: 以紫云县白云小学为例[J]. 中国岩溶, 2020, 39(1): 80-87.YANG Yuanli, MENG Fantao, LI Minghui. Study on the genesis mechanism and prevention and control measures of shallow overburden karst collapse in central Guizhou area: An example of Baiyun primary school, Ziyun county, Guizhou Province[J]. Carsologica Sinica, 2020, 39(1): 80-87. [2] 陈雨昂, 唐荣, 方建, 孔锋. 2014-2018年中国城市路面塌陷时空规律与原因分析[J]. 水利水电技术, 2020, 51(7): 108-116.CHEN Yu'ang, TANG Rong, FANG Jian, KONG Feng, KONG Feng. Analysis on spatio-temporal law and causation of urban road collapse in China from 2014 to 2018[J]. Water Resources and Hydropower Engineering, 2020, 51(7): 108-116. [3] 刘道涵, 张欣, 何军, 邬健强, 刘磊. 地面核磁共振测深方法在武汉市岩溶地面塌陷探测中的应用研究[J]. 中国岩溶, 2022, 41(1): 13-20.LIU Daohan, ZHANG Xin, HE Jun, WU Jianquang, LIU lei. Study on the application of surface nuclear magnetic resonance in the detection of karst collapse in Wuhan[J]. Carsologica Sinica, 2022, 41(1): 13-20. [4] Gutiérrez F, Parise M, De Waele J, Jourde H. A review on natural and human-induced geohazards and impacts in karst[J]. Earth-Science Reviews, 2014, 138: 61-88. doi: 10.1016/j.earscirev.2014.08.002 [5] 吴亚楠, 杨云涛, 焦玉国, 刘志涛, 王延岭, 翟代廷,周绍智, 魏凯, 程凤. 山东省岩溶塌陷发育特征及诱因分析[J]. 中国岩溶, 2023, 42(1): 128-138, 148.WU Ya’nan, YANG Yuntao, JIAO Yuguo, LIU Zhitao, WANG Yanling,ZHAi Daiting,ZHOU Shaozhi, WEI Kai, CHENG fen. Analysis on development characteristics and inducement of karst collapse in Shandong Province[J]. Carsologica Sinica, 2023, 42(1): 128-138, 148. [6] Kiyeon Kim, Joonyoung Kim, Tae-Young Kwak, hoong-Ki Chung. Logistic regression model for sinkhole susceptibility due to damaged sewer pipes[J]. Natural Hazards, 2018, 93: 765-785. doi: 10.1007/s11069-018-3323-y [7] Zhang N, Shen J S, Lin C. Investigation of a large ground collapse and countermeasures during mountain tunnelling in Hangzhou: a case study[J]. Bulletin of Engineering Geology and the Environment, 2019, 78: 991-1003. doi: 10.1007/s10064-017-1098-0 [8] 吴远斌, 刘之葵, 殷仁朝, 雷明堂, 戴建玲, 罗伟权, 潘宗源. 基于AHP和GIS技术的湖南怀化地区岩溶塌陷易发性评价[J]. 中国岩溶, 2022, 41(1): 21-33. doi: 10.11932/karst2021y44WU Yuanbin, LIU Zhikui, YIN Renchao, LEI Mingtang, DAI Jianling, LUO Weiquan, PAN Zongyuan. Evaluation of karst collapse susceptibility in Huaihua area, Hunan Province based on AHP and GIS[J]. Carsologica Sinica, 2022, 41(1): 21-33. doi: 10.11932/karst2021y44 [9] 吴亚楠, 王延岭, 周绍智, 唐丽伟, 焦玉国. 基于综合指数法的泰莱盆地岩溶塌陷风险性评价[J]. 中国岩溶, 2020, 39(3): 391-399.WU Yanan, WANG Yanling, ZHOU Shaozhi, TANG Liwei, JIAO Yuguo. Risk assessment of karst collapse in the Tailai basin based on the synthetic index method[J]. Carsologica Sinica, 2020, 39(3): 391-399. [10] 张杰, 毕攀, 魏爱华, 陶志斌, 朱慧超. 基于模糊综合法的烟台市栖霞中桥岩溶塌陷易发性评价[J]. 中国岩溶, 2021, 40(2): 215-220.ZHANG Jie, BI Pan, WEI Aihua, TAO Zhibing, ZHU Huichao. Assessment of susceptibility to karst collapse in the Qixia Zhongqiao district of Yantai based on fuzzy comprehensive method[J]. Carsologica Sinica, 2021, 40(2): 215-220. [11] 苏鸿宇. 英州大道南附近发生地陷, 英城街道全力协调推进安置工作[EB/OL]. 英德: 英德市人民政府门户网站, 2020. http://www.yingde.gov.cn/zwgk/zwdt/zjdt/content/post_1314539.htmlSU Hongyu. Ground subsidence near Yingzhou Avenue South, Yingcheng Street coordinates all efforts to promote resettlement[EB/OL]. Yingde: Yingde City People's Government Portal, 2020 [12] Kromhout C, Baker A E. sinkhole vulnerability mapping: results from a pilot study in north central Florida[J]. 2015. [13] 罗小杰. 也论覆盖型岩溶地面塌陷机理[J]. 工程地质学报, 2015, 23(5): 886-895.LUO Xiaojie. Further discussion on mechanism of covered karst ground collapse[J]. Journal of Engineering Geology, 2015, 23(5): 886-895. [14] 易顺民, 卢薇, 周心经. 广州夏茅村岩溶塌陷灾害特征及防治对策[J]. 热带地理, 2021, 41(4) : 801-811.YI Shumin, LU Wei, ZHOU Xinjing. The Formation Investigation and Remediation of Sinkhole in the Xiamao Village, Guangzhou[J] Tropical Geography, 2021, 41(4) : 801-811. [15] 赵博超, 朱蓓, 王弘元, 赖柄霖.浅谈岩溶塌陷的影响因素与模型研究[J]. 中国岩溶, 2015, 34(5): 515-521.ZHAO Bochao, ZHU Bei, WANG Hongyuan, LAI Binglin.Influence factors and mathematical models of karst collapses[J].Carsologica Sinica, 2015, 34(5): 515-521. [16] 黄敬军, 武鑫, 缪世贤, 崔龙玉, 顾春芬, 姜素. 江苏徐州新生街岩溶塌陷形成条件及与岩溶水水位变化的关系探讨[J]. 中国地质灾害与防治学报, 2017, 28(4): 125-129, 136.HUANG Jingjun, WU Xin, MIU Shixian, CUI Longyu, GU Chunfen, JIAN Su. The relationship between the karst collapse formation condition and the karst water level change in Xuzhou Xinsheng street, Jiangsu[J].The Chinese Journal of Geological Hazard and Control, 2017, 28(4): 125-129, 136. [17] 陈余道, 朱学愚, 蒋亚萍. 黏性土土洞形成的水化学侵蚀实验[J]. 水文地质工程地质, 1997(1): 29-32.CHEN Yudao, ZHU Xueyu, JIANG Yaping. Hydrochemical erosion test of formation of clayey soil hole[J]. Hydrogeology & Engineering Geology, 1997(1): 29-32. [18] 谢银财, 于奭, 缪雄谊, 李军, 何师意, 孙平安. 青藏高原流域岩石风化机制及其CO2消耗通量: 以拉萨河为例[J]. 地学前缘, 2023, 30(5): 510-525.XIE Yincai, YU Shi, MIAO Xiongyi, LI Jun, HE Shiyi, SUN Pingan. Chemical weathering and CO2 consumption flux in Tibetan Plateau: A case of Lhasa River Basin[J]. Earth Science Frontiers, 2023, 30(5): 510-525. [19] 蒋小珍, 冯涛, 郑志文, 雷明堂, 张伟, 马骁, 伊小娟. 岩溶塌陷机理研究进展[J]. 中国岩溶, 2023, 42(3): 517-527JIANG Xiaozhen, FENG TAO, ZHENG Zhiwei, LEI Mingtang, ZHANG Wei, MA Xiao, YI Xiaojuan. A review of karst collapse mechanisms[J]. Carsologica Sinica, 2023, 42(3): 517-527 [20] Saaty T L. Multicriteria decision making: The analytic hierarchy process[M]. RWS Publ , 1996. [21] 莫建飞, 陆甲, 李艳兰, 陈燕丽. 基于GIS的广西洪涝灾害孕灾环境敏感性评估[J]. 灾害学, 2010, 25(4): 33-37.MO Jianfei, LU Jia, LI Yanlan, CHEN Yanli.GIS-based Sensitivity Assessment on Environment of Developing Flood Hazards in Guangxi Province[J]. Journal of Catastrophology, 2010, 25(4): 33-37. -
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