基于GDIV模型的大渡河中游地区滑坡危险性评价与区划

阳清青; 余秋兵; 张廷斌; 易桂花; 张恺

doi:10.16031/j.cnki.issn.1003-8035.202208014

基于GDIV模型的大渡河中游地区滑坡危险性评价与区划

doi: 10.16031/j.cnki.issn.1003-8035.202208014

1.
成都理工大学地球科学学院，四川成都　610059
2.
四川省冶金地质勘查局六〇六大队，四川成都　611730
3.
成都理工大学旅游与城乡规划学院，四川成都　610059

基金项目: 国家自然科学基金项目（41801099）

详细信息

作者简介:
阳清青（1997-），女，四川南充人，硕士研究生，主要从事环境遥感研究。E-mail：2020050063@stu.cdut.edu.cn

通讯作者:
余秋兵（1989-），男，四川南充人，硕士，工程师，主要从事地质构造与地质调查研究工作。E-mail：yu8ye4@yeah.net

中图分类号: P642.22
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出版历程
- 收稿日期: 2022-08-08
- 修回日期: 2023-01-14
- 网络出版日期: 2023-07-12
- 刊出日期: 2023-10-31

Landslide hazard assessment in the middle reach area of the Dadu River based on the GDIV model

1.
College of Earth Science, Chengdu University of Technology, Chengdu, Sichuan　610059, China
2.
606 Bringade of Sichuan Bureau of Metallurgical Geology Exploration, Chengdu, Sichuan　611730, China
3.
College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu, Sichuan　610059, China

摘要

摘要: 区域地质灾害评价是减灾防治的重要非工程手段，构建区域滑坡危险性评价模型，对提高地质灾害评价精度和防治效率具有重要意义。文章以滑坡频发的大渡河中游地区为研究区，初选高程、坡度、坡向、地震动参数、土壤类型、工程地质岩组、年平均降雨量和地形湿度指数（TWI）等13个因子，建立滑坡危险性初级评价指标体系。考虑各因子对滑坡形成贡献程度的不同和目前常权栅格叠加方式对滑坡危险性评价结果精度的影响，引入了地理探测器和变权栅格叠加，构建了地理探测器、信息量法和变权栅格叠加的组合模型（GDIV模型）。基于2021年四川省1∶50 000地质灾害风险调查中313处滑坡地质灾害隐患点，开展基于GDIV模型的大渡河中游地区滑坡危险性评价，并与逻辑回归模型和信息量模型的组合模型（LRI模型）评价结果进行对比分析。结果表明：研究区以中危险及以下危险区为主，占总面积的78.3%，极高和高危险区主要分布在大渡河、革什扎河和东谷河两岸的低海拔地区；与LRI模型相比，基于GDIV模型的评价结果精度更高，其受试者工作特征（ROC）曲线的线下面积（AUC）值为0.917。文章提出的GDIV模型提高了区域滑坡危险性评价精度，可为类似地区地质灾害评价提供方法参考。
- 地理探测器 /
- 信息量法 /
- 变权栅格叠加 /
- 危险性评价 /
- 大渡河中游地区
Abstract: Regional geological hazard assessment is an important non-engineering approach for disaster reduction and prevention. Constructing a regional landslide hazard assessment model is of great significance in improving the accuracy of geological hazard evaluation and the efficiency of prevention. This study focuses on the frequent landslide occurrence in the middle reach area of the Dadu River and selects 13 primary factors, including elevation, slope, aspect, seismic parameters, soil type, engineering geological lithology, annual average rainfall, and topographic wetness index (TWI), to establish a primary evaluation index system for landslide hazard. Considering the varying contributions of each factor to landslide formation and the impact of the commonly used weighted raster superposition methods on assessment accuracy, the geographic detector and variable weight raster overlay techniques are introduced, leading to the development of the GDIV model. Using data from 313 landslide hazard points identified in the 2021 geological hazard risk survey at a scale of 1∶50,000 in Sichuan Province, the landslide hazard assessment in the middle reach area of the Dadu River basin is conducted based on the GDIV model, and the evaluation results are compared with those of the LRI model. The results show that the study area is predominantly characterized by middle and lower risk areas, accounting for 78.3% of the total area. The extremely high and high-risk areas are primarily located in the low-elevation regions along the banks of Dadu River, Geshizha River, and Donggu River. Compared to the LRI model, the evaluation results based on the GDIV model exhibit higher accuracy, with an area under the receiver operating characteristics (ROC) curve of 0.917. The GDIV model proposed in this paper improves the accuracy of regional Landslide hazards assessment, and serves as a valuable reference for similar geological disaster evaluations in other areas.
- geographic detectors /
- informative quantity /
- variable weight raster overlay /
- hazards assessment /
- middle reach area of the Dadu River

HTML全文

图 1 大渡河中游地区滑坡分布图和地质条件背景图

Figure 1. Map of landslide distribution and geological conditions in the middle reach area of Dadu River

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图 2 大渡河中游地区滑坡危险性初级评价指标体系分级图

Figure 2. Grading chart of the primary hazard assessment index system for landslides in the middle reach area of Dadu River Basin

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图 3 变权栅格叠加过程

Figure 3. The variational raster overlay process

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图 4 GDIV模型计算流程图

Figure 4. The flowchart of GDIV model calculation process

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图 5 滑坡危险性区划图

Figure 5. Landslide hazard zoning map

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图 6 滑坡危险性评价结果ROC曲线

Figure 6. ROC curve of landslide hazard evaluation results

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表 1 交互作用探测器因子关系

Table 1. Factor relationships of interaction detectors

因子关系	交互作用
q(X₁∩X₂)<Min(q(X₁), q(X₂))	非线性减弱
Min(q(X₁), q(X₂))< q(X₁∩X₂)< Max (q(X₁), q(X₂))	单因子非线性减弱
q(X₁∩X₂)> Max (q(X₁), q(X₂))	双因子增强
q(X₁∩X₂)= q(X₁)+q(X₂)	独立
q(X₁∩X₂)> q(X₁)+q(X₂)	非线性增强

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表 2 滑坡初级评价指标q值统计

Table 2. Statistical analysis of primary evaluation index q-values for landslides

类别	指标	q值	p值
地质特征	工程地质岩组（X₁）	0.156	0.000
地质特征	与断层距离（X₂）	0.087	0.000
地震	地震动参数（X₃）	0.164	0.000
地形地貌	高程（X₄）	0.583	0.000
	坡度（X₅）	0.021	0.023
	坡向（X₆）	0.038	0.003
	地形湿度指数（X₇）	0.017	0.297
	归一化植被指数（X₈）	0.072	0.000
	土壤类型（X₉）	0.415	0.000
地表水系	与河流距离（X₁₀）	0.158	0.000
地表水系	径流强度指数（X₁₁）	0.032	0.015
降雨	年平均降雨量（X₁₂）	0.182	0.000
人类活动	与道路距离（X₁₃）	0.115	0.000

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表 3 部分滑坡初级评价指标交互作用

Table 3. Interactions of primary evaluation indicators for landslides

X_i∩X_j	q(X_i)	q(X_j)	q(X_i∩X_j)	q(X_i)+q(X_j)	交互类型
X₄∩X₁	0.583	0.156	0.736	0.739	双因子增强
X₃∩X₄	0.164	0.583	0.676	0.747	双因子增强
X₉∩X₄	0.415	0.583	0.596	0.998	双因子增强
X₁₀∩X₄	0.158	0.583	0.603	0.741	双因子增强
X₁₃∩X₄	0.115	0.583	0.597	0.698	双因子增强
X₁₂∩X₄	0.182	0.583	0.672	0.765	双因子增强
X₉∩X₃	0.415	0.164	0.537	0.579	双因子增强
X₉∩X₁	0.415	0.156	0.555	0.571	双因子增强
X₉∩X₁₀	0.415	0.158	0.434	0.573	双因子增强
X₉∩X₁₃	0.415	0.115	0.428	0.53	双因子增强
X₉∩X₁₂	0.415	0.182	0.527	0.597	双因子增强
X₁₀∩X₃	0.158	0.164	0.312	0.322	双因子增强
X₁₀∩X₁	0.158	0.156	0.344	0.314	非线性增强
X₁₃∩X₃	0.115	0.164	0.276	0.279	双因子增强
X₁₃∩X₁	0.115	0.156	0.278	0.271	非线性增强
X₃∩X₁	0.164	0.156	0.329	0.320	非线性增强
X₁₃∩X₁₀	0.115	0.158	0.226	0.273	双因子增强
X₁₀∩X₁₂	0.158	0.182	0.343	0.340	非线性增强
X₁₃∩X₁₂	0.115	0.182	0.292	0.297	双因子增强
X₃∩X₁₂	0.164	0.182	0.269	0.346	双因子增强
X₁₂∩X₁	0.182	0.156	0.348	0.338	非线性增强

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表 4 危险性评价因子分级与信息量值

Table 4. Grading and information value of hazard evaluation factors

评价因子	分级	信息量值	评价因子	分级	信息量值
高程/m	<2 700	2.058	年平均降雨量/mm	<750	−0.557
	2 700～3 200	1.308		750～775	0.438
	3 200～3 600	−1.37		775～800	−1.014
	3 600～4 000	−2.445		800～840	−0.055
	4 000～4 400	−3.76		840～880	−0.404
	> 4400	—		>880	−0.231
土壤类型	淋溶土	1.685	地震动参数	<0.1	0.151
	半淋溶土	—		0.1～0.15	0.464
	初育土	−3.921		0.15～0.2	−1.059
	高山土	0.107		0.2～0.3	—
	人为土	1.429	与道路距离/m	<100	1.500
	铁铝土	0.890		100～200	1.227
与河流距离/m	<400	−1.204		200～300	1.148
	400～800	−0.826		300～400	1.053
	800～1 200	−0.025		400～500	0.789
	1 200～1 600	0.004		>500	−0.335
	1 600～2 000	0.577
	>2 000	1.038
工程地质岩组	坚硬岩	0.023
	较坚硬岩	0.443
	较软岩	1.878
	松散土类	−1.086

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表 5 滑坡危险性评价因子逻辑回归分析结果

Table 5. Results of logistic regression analysis for landslide hazard evaluation factors

评价因子	B	SE	Wald	df	sig	Exp(B)
高程	4.992	0.551	82.210	1	0.000	147.24
土壤类型	3.001	0.550	29.785	1	0.000	20.110
工程地质岩组	1.606	0.837	3.387	1	0.000	4.666
年平均降雨量	1.103	0.379	8.468	1	0.000	3.013
与道路距离	0.995	0.396	2.573	1	0.000	2.435
地震动参数	0.802	0.469	1.657	1	0.000	1.830
与河流距离	0.148	0.398	5.259	1	0.001	0.739
常数	−7.132	0.696	104.815	1	0.000	0.001
注：B为模型中各变量的回归系数、SE是标准差、Wald是卡方统计、Sig为显著性水平,df和Exp(B)为逻辑回归的结果参数。

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表 6 滑坡危险性评价因子权重值

Table 6. Weight values of landslide hazard assessment factors

因子	q值	权重
高程	0.583	0.329
土壤类型	0.415	0.234
年平均降雨量	0.182	0.103
地震动参数	0.164	0.092
与河流距离	0.158	0.089
工程地质岩组	0.156	0.088
与道路距离	0.115	0.065

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