Study on susceptibility of karst collapse based on normal cloud model in Yonghe town, Liuyang City
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摘要:
在对浏阳市永和镇既有岩溶塌陷进行详细勘查的基础上，选取了岩溶发育程度（钻孔溶蚀率）、与区内断层的距离、上覆土层厚度、岩溶水特征、与人工抽水漏斗中心的距离以及地面塌陷现状（地面塌陷密度）等6个岩溶塌陷易发性评价因子，提出了赋值条件及赋值范围，并根据既有塌陷的评价因子评分状况，采用熵值法获取了评价因子的权重，基于正态云模型对研究区岩溶塌陷灾害的易发性进行了评价。正态云模型评价结果中既有岩溶塌陷均分布在岩溶塌陷高易发区，而层次分析法评价结果中3个既有塌陷分布在岩溶塌陷中易发区，说明正态云模型在处理类似岩溶塌陷易发性评价等模糊性及随机性问题时比层次分析法更精准。
Abstract:Ground collapse is a common geological disaster in karst area, which would bring great harm to people's life and property. The zoning evaluation on karst collapse susceptibility is beneficial to the classification and treatment of the disaster in order to ensure safety and economy. Karst ground collapse is characterized by suddenness, concealment, multi-factor, randomness and fuzziness; therefore, it is difficult to be fully quantified. The normal cloud model could effectively reflect the fuzziness and randomness of objective things, and integrate them to form the mapping between qualitative and quantitative analyses. In this paper, based on the normal cloud model, the study on susceptibility of karst collapse in Yonghe town, Liuyang City has been conducted in order to provide a basis for the classification and treatment of ground collapse in this area, and also provide a reference for the susceptibility evaluation of karst collapse in other areas.
The study area is located to the northeast of Liuyang City, 24 km away from it, and its administrative division is located in Yonghe town, Liuyang City, Hunan Province. Ground collapses in this area are all developed in the distribution area of soluble rock layer. The most developed layer is the second member of the Lower Permian Qixia Formation (P1q2) with thick-layered carbonate rocks. Secondarily, ground collapses are distributed in the middle and upper Carboniferous Hutian Group (C2+3ht). The study area covers an area of 14 km2, and 38 karst collapses have occurred in this area so far. Among these collapses, 22 have been surveyed and filled. Sixteen collapses, four of which have been filled, have been investigated in detail. The collapse sites are mainly distributed in the region of soluble rock where faults are developed and surface water and groundwater are closely connected. Geographically, the collapse sites are mainly distributed in Yueshan Formation-Oujia Formation-Dahe Formation, Yongfu village, Yonghe town, Juxiang community-the old street of Yonghe-Huayuan village, Yanxi town, Nanshan Formation-Lizhen Primary School (old)-Xinwan Formation-Xinping Formation.
Six evaluation indexes of karst collapse susceptibility were selected in this study, including karst development degree (dissolution rate of borehole), distance from the fault in the area, thickness of overlying soil layer, characteristics of karst water, distance from the pumping funnel center and current situation of ground collapse (ground collapse density). Firstly, according to the detailed exploration results of existing subsidence pits, the weights of six evaluation indicators of 16 existing subsidence pits were calculated and assigned based on entropy weight method. The calculation results indicate that the weight of development density of existing ground collapses is the largest, and that of the fluctuation amplitude of groundwater is the smallest. Secondly, the scoring standard for the risk level of normal cloud model was determined, and the susceptibility of karst collapse in each unit area in the study area was evaluated combined with the weight of each evaluation index. The evaluation results show that all the karst collapses that occurred are distributed in the area highly subject to karst collapse, indicating that the zoning in the evaluation of this study is reasonable.
When the normal cloud model is used to evaluate the susceptibility of karst collapse, the size of grid cells could be controlled by the cloud similarity of comprehensive risk evaluation of karst collapse. For areas with high similarity and small cloud droplet dispersion, the unit area of each evaluation could be appropriately increased in order to reduce the evaluation workload. In addition, the high risk area calculated by analytic hierarchy process is 10.8% larger than that by normal cloud method, but three existing collapse pits distributed in the area at medium-level risk of karst collapse, indicating that normal cloud model takes more advantages than analytic hierarchy process in dealing with fuzzy and random problems such as the risk evaluation of karst collapse.
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Key words:
- karst /
- surface collapse /
- evaluation factors /
- normal cloud model /
- susceptibility
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表 1 评价因子及赋值表
Table 1. Table of evaluation factors and assignment
编号 评价因子名称 赋值条件 取值范围 1 钻孔溶蚀率/% >10 8 3~10 2~8 <3 2 2 与区内断层距离/m 0~50 8 50~100 2~8 >100 2 3 上覆土层厚度/m 总厚≤10，上部黏土≤3 8 10<总厚≤20，上部黏土≤3 2~8 总厚>20，上部黏土>3 2 4 地下水波动幅度/m·a−1 >8 8 3~8 2~8 <3 2 5 与抽水漏斗中心距离/m 0~200 8 200~500 2~8 >500 2 6 已有地面塌陷发育密度(个/10 km2) >10 8 2~10 2~8 <2 2 
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表 2 既有塌陷坑调查赋值
Table 2. Investigation and assignment of existing collapse pits
序号 钻孔溶蚀率 与区内断层距离 上覆土层厚度 地下水波动幅度 与抽水漏斗中心距离 已有地面塌陷发育密度 塌陷1 8.0 8.0 8.0 8.0 8.0 8.0 塌陷2 5.2 8.0 6.8 8.0 8.0 8.0 塌陷3 7.0 7.6 8.0 8.0 6.8 8.0 塌陷4 7.6 7.2 8.0 8.0 6.2 8.0 塌陷5 7.5 6.0 8.0 7.5 7.8 8.0 塌陷6 8.0 7.6 8.0 7.5 7.5 8.0 塌陷7 8.0 6.4 8.0 8.0 6.7 8.0 塌陷8 7.0 7.8 6.8 8.0 6.7 6.8 塌陷9 7.2 6.5 7.8 7.8 8.0 6.8 塌陷10 6.5 6.8 7.5 8.0 7.5 6.8 塌陷11 6.8 6.2 7.2 7.6 7.3 6.5 塌陷12 6.5 8.0 8.0 6.5 8.0 5.2 塌陷13 5.8 8.0 7.5 5.2 5.2 5.2 塌陷14 5.6 7.5 7.6 8.0 5.8 5.8 塌陷15 6.7 6.5 8.0 7.6 2.0 7.6 塌陷16 7.2 7.6 8.0 8.0 2.0 8.0
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表 3 评价因子权重
Table 3. Evaluation factor weight
评价
因子钻孔
溶蚀率与区内断层
距离上覆土层
厚度地下水波动
幅度人工抽
排地下水已有地面塌陷
发育密度权重 0.179 93 0.124 26 0.199 72 0.091 81 0.175 45 0.228 83
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表 4 单元点内勘查点评价因子赋值
Table 4. Evaluation factor assignment of exploration spots in unit points
序号 钻孔
溶蚀率与区内断层
距离上覆土层
厚度地下水波动
幅度人工抽
排地下水已有地面塌陷
发育密度勘查点1 7.6 8 7.6 6.2 7.2 7.0 勘查点2 7.6 8 5.4 6.2 7.2 7.0 勘查点3 7.6 8 7.2 6.2 6.8 7.0 勘查点4 7.6 7.2 7.2 5.2 6.8 7.0 勘查点5 7.0 7.2 8 5.2 5.6 7.0 勘查点6 7.0 6.8 8 5.2 5.6 7.0
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表 5 岩溶塌陷评分标准正态云模型特征参数
Table 5. Characteristic parameters of scoring standard of normal cloud model for karst collapse
风险等级 高易发区 中易发区域 低易发区 数字特征 （7，0.333，0.1） （4.8，0.4，0.1） （2.8，0.4，0.1）
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表 6 岩溶塌陷综合风险评估云的数字特征
Table 6. Digital characteristics of comprehensive risk evaluation cloud for karst collapse
项目总指标 $ {E}_{x} $ $ {E}_{n} $ $ He $ 岩溶塌陷综合评价云 $ C $ 6.983 6 0.485 700 031 0.231 6
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表 7 岩溶塌陷综合风险评价云相似度
Table 7. Cloud similarity of comprehensive risk evaluation for karst collapse
相似度 高易发区 中易发区域 低易发区 评估结果 岩溶塌陷综合
风险评价云$ C $ 0.804 4 0.010 1 0 高易发区
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表 8 地面塌陷易发性分区一览表
Table 8. Zoning of ground collapse susceptibility
灾种 综合分区 面积/km2 分布区域 地面塌陷 高易发区 1.56 花园村－菊香社区－永福村一带 0.15 永和中学－佳成村南山组一带 0.58 李贞小学（旧）－佳成村新湾组－佳成村新平组一带 0.12 井泉村牛车组一带 0.93 铁山村－宝山村一带 中等易发区 9.18 花园村－菊香社区－佳成村－宝山河沿岸一带 低易发区 5.91 危险性大区、中等区之外
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