海底滑坡动力侵蚀机理研究：回顾与展望

殷跃平; 王文沛; 邢爱国; 黄波林; 李滨; 韩雷岩; 金少强; 杨勇; 张晨阳

doi:10.16031/j.cnki.issn.1003-8035.202502015

海底滑坡动力侵蚀机理研究：回顾与展望

殷跃平^1,,
王文沛^1, ,,
邢爱国²,
黄波林³,
李滨⁴,
韩雷岩⁵,
金少强^{5, 6},
杨勇⁷,
张晨阳⁸

1.
中国地质环境监测院（自然资源部地质灾害防治技术指导中心），北京　100081
2.
上海交通大学船舶海洋与建筑工程学院，上海　200240
3.
三峡大学土木与建筑学院，湖北宜昌　443002
4.
中国地质科学院地质力学研究所，北京　100081
5.
中国三峡新能源（集团）股份有限公司，北京　101199
6.
三峡新能源海上风电运维江苏有限公司，江苏盐城　224008
7.
南京工业大学土木工程学院，江苏南京　211816
8.
香港理工大学土木及环境工程学系，香港　999077

基金项目: 国家重点研发计划项目（2022YFC3004301）；甘肃省联合科研基金项目（24JRRA800）

详细信息

作者简介:
殷跃平（1960—），男，贵州独山人，中国工程院院士，研究员，从事地质灾害防治与研究工作。E-mail：yyueping@mail.cgs.gov.cn

通讯作者:
王文沛（1985—），男，江苏扬中人，土木工程专业，博士，正高级工程师，主要从事地质灾害防治工作。E-mail：jcywangwenpei@mail.cgs.gov.cn

中图分类号: P767；P642.22
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出版历程
- 收稿日期: 2025-02-03
- 修回日期: 2025-02-10
- 录用日期: 2025-02-13
- 网络出版日期: 2025-02-16
- 刊出日期: 2025-02-24

Research on dynamic erosion mechanism of submarine landslide: Review and prospects

1.
China Institute of Geo-Environment Monitoring （Guide Center of Prevention Technology for Geo-Hazards, MNR）, Beijing　100081, China
2.
School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai　200240, China
3.
School of Civil Engineering and Architecture, Three Gorges University, Yichang, Hubei　443002, China
4.
Institute of Geomechanics, Chinese Academy of Geological Sciences, Beijing　100081, China
5.
China Three Gorges Renewables （Group） Co. Ltd., Beijing　101199, China
6.
CTG Offshore Wind Power Maintenance Jiangsu Co. Ltd., Yancheng, Jiangsu　224008, China
7.
College of Civil Engineering, Nanjing Tech University, Nanjing, Jiangsu　211816, China
8.
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong　999077, China

摘要

摘要:
海底滑坡会对海上风电、海底光缆、海洋平台等基础设施造成严重破坏，给“建设海洋强国”重大战略和保障海洋工程地质安全带来了严峻挑战。文章系统回顾了海底滑坡浊流地质灾害的研究历程，总结了国内外关于海底滑坡浊流链动特征、动力侵蚀类型、触发-演化-运移-侵蚀沉积机制、侵蚀理论模型及其对海底隆起、峡谷、盆地等复杂地貌的影响，提出了定量、多相、全过程、侵蚀-流态转化耦合的海底滑坡浊流动力侵蚀研究思路。最后，针对海上风电、海洋资源开发、海洋交通运输、海洋工程装备等重大工程的规划建设，展望了海底滑坡浊流基底易蚀结构地质模型和判识技术、滑坡-碎屑流-浊流灾害链复合、叠合及异构等全过程动力侵蚀力学模型及边界层动力侵蚀防控理论技术问题的研究方向。
- 海底滑坡 /
- 浊流 /
- 碎屑流 /
- 超临界流 /
- 动力侵蚀 /
- 防治
Abstract:
The geological hazards of submarine landslides can cause serious damage to infrastructure such as offshore wind power, submarine optical cables, and marine platforms, posing a serious challenge to the major strategic task of building a maritime power and ensuring the geological safety of marine engineering. The article systematically reviews the research process of submarine landslide turbidity current geological hazards, summarizes the dynamic characteristics of submarine landslide-turbidity flow chain, dynamic erosion types, mechanisms of triggering, evolution, migration, erosion and sedimentation, theoretical models of erosion, and the influence of complex landforms such as uplift, canyons, and basins. A novel dynamic erosion approach is put forward of submarine landslide-turbidity flow chain, including quantitative, multiphase, whole process, erosion flow-state transformation. Finally, in view of the development of major projects such as offshore wind power, marine resource development, marine transportation, and marine engineering equipment, the geological model and identification technology are discussed of the erosion-prone structure of submarine landslide landslide-turbidity flow chain, as well as the composite, overlapping, and heterogeneous dynamic erosion mechanic model of the disaster chain, and the issues of prevention and control of boundary layer dynamic erosion.
- submarine landslide /
- turbidity flow /
- debris flow /
- supercritical flow /
- dynamic erosion /
- prevention and control

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0. 引言

云南以红河深大断裂为界，滇西为高山纵谷区，滇东为喀斯特高原^[1]，滇东北为地质灾害高易发区，滇西地质灾害易发性南相对较弱^[2]。罗平县地处云南滇东喀斯特高原地区，是典型的岩溶山区。目前，崩滑易发性制图主要分为定性、定量和机器学习3种方法^{[3 − 4]}。定性分析是通过对成因机制的全面认识，基于专家经验和知识确定评价因子权重，定量分析方法通过数学或数值算法估计滑坡易发性^[5]。定性分析方法主要有层次分析法^{[6 − 8]}，定量分析方法主要有频率比法^{[9 − 10]}、信息量法^{[11 − 12]}，机器学习方法有逻辑回归法、随机森林法、K近邻、支持向量机和神经网络等^{[13 − 15]}。国内外学者进行研究分析，并采用多种研究方法进行对比，吉日伍呷等^[15]通过逻辑回归、K近邻、朴素贝叶斯和随机森林算法对鲁甸进行地震滑坡易发性评价，并得出统计建模地更多的是寻找变量之间的可解释关系；樊芷吟等^[16]通过信息量法+Logistic回归模型对汶川县进行易发性评价；张晓东^[17]通过定量信息量法和确定性系数法分别与Logistic回归耦合对宁夏盐池县进行易发性评价，得出耦合模型结果均优于单一模型评价结果。根据以上研究，易发性评价模型首先选取崩滑评价影响因子，再通过分类分级进行计算，根据不同方法得出的结果基于ArcGIS进行等级划分。信息量法只考虑了评价因子分类分级状态下的权重，其优点在于原理简单，易于实现；缺点在于无法体现所选取的评价因子的权重。因此选取层次分析法和逻辑回归法分别赋予评价因子的权重，其中层次分析法依据崩滑灾害成因分析，通过专家法构建矩阵计算得出评价因子的权重，逻辑回归法是依据样本数据和连接方法，通过两种方法得出评价因子的权重分别与信息量法进行耦合，其耦合模型将评价因子的权重和分类分级的权重叠加得到综合权重，降低了单一评价模型人为主观性因素的影响，论证不同方法评价结果的准确性。

本文以罗平县作为研究区域，基于野外详实的地质灾害调查成果，综合分析孕灾地质条件和崩滑点分布规律，选取岩土体（工程岩组）、地形地貌（坡度、坡向、高程、起伏度、曲率、地貌类型）、地质构造（距断裂距离）、气象水文（距河流距离）等评价因子。采用信息量法、加权信息量法、信息量-逻辑回归耦合法构建易发性评价模型进行对比分析，并对评价结果进行精度检验分析，选取精度最高模型易发性分布图，可为罗平县今后地质灾害治理提供参考依据，对今后城市的发展和防灾减灾有重要意义，也可为岩溶地区地质灾害易发性评价提供参考。

1. 研究区概况及数据来源

1.1 研究区概况

研究区（罗平县）位于云南省曲靖市东部。东西最大横距75 km，南北最大纵距99 km。相对高差为1705 m，全县面积3018 km²，山区面积占78%，坝区面积占22%（图1）。研究区西部和北部属于岩溶盆地地貌和岩溶低中山地貌，中部属岩溶断陷湖形盆地，东部和南部受九龙河和南盘江流域侵蚀切割，形成峰林洼地和岩溶中山地貌。区内地层出露主要有古生界泥盆系（D）浅灰、深灰色中厚层状灰岩、泥灰岩、泥质白云岩；石炭系（C）深灰、灰黑色块状灰岩、白云质灰岩、泥质灰岩；古生界二叠系（P）灰、深灰色厚层块状、生物碎屑灰岩，结晶灰岩夹虎斑状灰岩及白云岩；中生界三叠系（T）上统为黄褐色粉砂岩、泥质粉砂岩及细砂岩、中统为深灰色灰岩夹泥质灰岩、中上部为黄色白云岩、下统为紫红色含长石粉细砂夹泥灰岩页岩及含铜页岩；新生界古近系（E）+新近系（N）褐黄紫红色砾岩、细砂岩及粉砂质泥岩、底部砾岩；新生界第四系（Q）细砂、砂砾石及砂质黏土。主要构造体系和构造型式有北东向构造、新华夏系构造、网状构造等。北东向构造为区内主导构造，是研究区内最重要的构造成分之一，主要断裂有：金鸡山断裂、长家湾断裂和腊庄断裂等。其次为新华夏系构造，多发育在褶皱边缘、密集成束、规模大、延伸远、呈舒缓波状，主要分布在西部及南盘江两岸。主要断裂有：洒土革断裂、大水塘断裂、罗格断裂等。

图 1 研究区概况

Figure 1. Overview of the study area

下载: 全尺寸图片幻灯片

1.2 数据来源

本研究数据主要包括：（1）12.5 m分辨率数字高程模型（DEM）收集自ASF，用于提取坡度、坡向、起伏度、曲率等评价因子；（2）1∶20万地质图收集自全国地质资料馆，用于提取岩性、断裂等因子；（3）1∶5万地理数据库提取水系；（4）历史崩滑数据：主要来自地矿眉山工程勘察院1∶5万全区调查结果，共154个崩滑灾害点的数据。

2. 研究方法

2.1 信息量模型

信息量模型是对崩滑历史数据进行统计分析，将影响崩滑的各因子的实测值转化为信息量值，来衡量崩滑的易发性^[18]。首先计算各评价因子的信息量值，再对各因子信息量值进行总和，作为崩滑易发性的综合指标^[19]。单因子信息量计算公式为：

I = \ln \frac{N_{j} / N}{S_{j} / S}

(1)

式中：I——评价因子j下的信息量；

N_j——评价因子j内发生的崩滑数；

N——研究区崩滑总数；

S_j——评价因子j下所占栅格数；

S——研究区总栅格数。

将每个评价单元各分类分级进行叠加计算，其地质灾害发生的总信息量计算公式为：

I_{j} = \sum_{i = 1}^{n} \ln \frac{N_{j} / N}{S_{j} / S}

(2)

式中：I_j——总信息量，为地质灾害易发性指数；I_j值越大且为正值则表示该单元内有利于崩滑发生。

2.2 层次分析模型

层次分析模型是一种将决策者定性判断和定量计算有效结合起来的分析方法。通过比较相邻影响因子的重要性^[19]，根据专家法构建判断矩阵^[20]：

A = (a_{i j}) = [\begin{array}{ccc} a_{11} & \dots & a_{1 n} \\ ⋮ & ⋱ & ⋮ \\ a_{n 1} & \dots & a_{n n} \end{array}]

(3)

式中：A——要素判断矩阵；

a_ij——因子i和因子j重要性比较的结果，有以下性质：

a_{i j} = \frac{1}{a_{j i}}, a_{i j} \neq 0, a_{i i} = 1, i, j = 1, 2, \dots, n

(4)

为保证求得的权重的正确性及合理性，还需要进行一致性检验。

C I = \frac{λ_{max} - n}{n - 1}

(5)

C R = C I / R I

(6)

式中：CI——一致性指标；

n——判断矩阵的阶数；

λ_max——判断矩阵的最大特征值；

CR——随机一致性比；当其<0.1时一致性检验通过；

RI——随机一致性指标。

2.3 逻辑回归模型

逻辑回归模型是一种研究二分类因变量常用的统计方法^[16,21]。通过研究崩滑易发性与评价因子之间的关系，预测崩滑发生的概率。其中自变量为评价因子指标值（x₁, x₂, ···, x_n），是否发生地质灾害作为因变量（分别用1和0代表崩滑点和非崩滑点）。逻辑回归函数如下：

Z = α + β_{1} x_{1} + β_{2} x_{2} + \dots + β_{n} x_{n}

(7)

P (y = 1) = \frac{1}{1 + e^{- z}}

(8)

式中：α——常数项；

x₁, x₂, ···, x_n——自变量；

β₁, β₂, ···, β_n——回归系数；

Z——崩滑发生的可能性与各评价因子之间的关系；

P——崩滑灾害发生的概率，范围0～1。

2.4 加权信息量模型

根据层次分析法得出各评价因子的权重值，结合信息量法各评价因子分类分级的信息量值，两者相乘得出加权信息量值，其计算公式可表示为：

I_{j} = \sum_{i = 1}^{n} ω_{i} \cdot I_{i} = \sum_{i = 1}^{n} ω_{i} \cdot \ln \frac{N_{i} / N}{S_{i} / S}

(9)

式中：I_j——加权信息量；

ω_i——每个评价因子的权重；

I_i——评价因子i的信息量值。

2.5 信息量-逻辑回归耦合模型

将信息量模型与逻辑回归模型进行耦合，通过逻辑回归确定评价因子的权重，可降低信息量模型评价因子分级的主观性影响。其原理将信息量模型中评价因子分类分级的信息量值作为逻辑回归模型中的自变量，建立回归方程进行逻辑回归运算，得出各评价因子的回归系数，以此为依据建立信息量-逻辑回归耦合模型。

3. 评价因子选取分级

3.1 评价因子选取分级

本文在罗平县资料收集和野外地质调查的基础上，选取岩土体（工程岩组）、地形地貌（坡度、坡向、高程、起伏度、曲率、地貌类型）、地质构造（距断裂距离）、气象水文（距河流距离）等评价因子进行分析。根据12.5 m×12.5 m栅格单元作为易发性评价的制图单元，通过对研究区评价因子与崩滑点数据进行归纳分析，得出各评价因子的分类分级处理（图2）。

图 2 崩滑评价因子分类分级图

Figure 2. Classification map of landslide susceptibility evaluation factors

下载: 全尺寸图片幻灯片

3.2 评价因子共线性诊断

进行逻辑回归时，需确保所选评价因子之间的相互独立，相关性高会出现多重共线性^{[22 − 23]}。采用容忍度（tolerance，TOL）和方差膨胀因子（variance inflation factor，VIF）对自变量进行多重共线性诊断：

V I F = \frac{1}{1 - R^{2}}

(10)

式中：R²——以x_i为因变量时对其他自变量回归的复测定系数；

TOL是VIF的倒数，当TOL大于0.1且VIF小于10时，则不存在多重共线性。

根据308个独立属性样本，提取每个样本的各类级信息量值，在SPSS软件中进行多重共线性诊断。结果显示对所选9个评价因子其VIF值在1～1.5（表1）。其VIF<5，表明各因子之间相互独立，不存在共线性。

表 1 评价因子VIF计算结果表

Table 1. Calculation results of VIF for evaluation factors

评价因子	TOL	VIF
工程岩组	0.818	1.222
坡度	0.656	1.524
坡向	0.954	1.048
高程	0.904	1.107
地貌类型	0.713	1.402
起伏度	0.669	1.495
曲率	0.970	1.031
距断裂距离	0.945	1.058
距河流距离	0.717	1.396

下载: 导出CSV

| 显示表格

3.3 评价因子相关性分析

崩滑的易发性与评价因子之间存在一定的相关性。为了保证各评价因子间的相互独立性和结果的可靠性，进行因子相关性检验^[24]。结果显示各评价因子之间的相关系数均<0.3（表2），评价因子之间的相关性较小，所以9个评价因子均可以进入模型。

表 2 评价因子之间的相关系数矩阵

Table 2. Correlation coefficient matrix of evaluation factors

评价因子	工程岩组	坡度	坡向	高程	地貌类型	起伏度	曲率	距断裂距离	距河流距离
工程岩组	1
坡度	0.07	1
坡向	−0.09	0.07	1
高程	0.03	−0.08	0.08	1
地貌类型	0.02	0.11	0.03	0.01	1
起伏度	0.11	0.03	0.04	0.00	0.01	1
曲率	0.07	−0.07	0.08	0.03	0.06	0.04	1
距断裂距离	0.09	−0.03	−0.04	0.06	0.09	−0.05	0.08	1
距河流距离	0.01	0.04	0.01	0.01	0.02	0.06	0.06	0.07	1

下载: 导出CSV

| 显示表格

4. 易发性评价结果

4.1 信息量模型评价结果

信息量模型中，崩滑的易发性与因子信息量值有关，信息量值越大且为正值则表示单元内崩滑越容易发生^{[25 − 28]}。根据已有154个地质灾害进行重分类统计，根据公式（1）计算各评价因子分类分级的信息量值（表3）。

表 3 评价因子分类分级信息量值

Table 3. Information value of classification levels for evaluation factors

评价因子	因子分级	崩滑数量	栅格数量	信息量值	加权信息量值
工程岩组	软硬相间碳酸盐岩夹碎屑岩岩组	2	205041	0.1347	0.0396
	块状结构坚硬玄武岩岩组	50	1817099	1.1717	0.3433
	坚硬层状碳酸盐岩岩组	97	15380267	−0.3014	−0.0883
	第四系冲洪积松散岩组	5	661729	−0.1206	−0.0353
坡度 /（°）	0～6	7	3162705	−1.3501	−0.2012
	6～12	33	3790902	0.0193	0.0029
	12～18	50	3707425	0.4571	0.0681
	18～24	31	3008341	0.1880	0.0280
	24～30	17	2054943	−0.0316	−0.0047
	30～36	5	1186737	−0.7064	−0.1053
	36～60	11	1091533	0.1657	0.0247
	60～90	0	33429	0	0
坡向	北	16	2219489	−0.1692	−0.0129
	东北	15	1920437	−0.0891	−0.0068
	东	22	2493207	0.0329	0.0025
	东南	31	2704414	0.2946	0.0224
	南	20	2304895	0.0161	0.0012
	西南	13	1974899	−0.2601	−0.0198
	西	17	2138397	−0.0714	−0.0054
	西北	20	2280277	0.0269	0.0020
高程/m	715～860	8	350496	0.9848	0.1468
	860～1200	8	1149066	−0.2025	−0.0233
	1200～1350	14	1312775	0.2239	0.4811
	1350～1500	26	3226787	−0.0097	−0.1776
	1500～1650	26	3079467	0.3627	−0.0402
	1650～1800	29	2366839	−0.1401	1.8675
	1800～1950	30	4047954	−0.5067	−0.8614
	1950～2420	13	2530843	−0.5067	−3.6224
地貌类型	岩溶低中山地貌	22	2824754	−0.0904	−0.0037
	构造侵蚀剥蚀地貌	18	1200919	0.5643	0.0231
	岩溶中山地貌	40	3833292	0.2021	0.0083
	岩溶盆地地貌	0	1948623	0	0
	峰林谷地地貌	0	91609	0	0
	峰丛洼地地貌	16	3752094	−0.6927	−0.0284
	断块上升岩溶地貌	1	177099	−0.4119	−0.0169
	断坳盆地	1	116724	0.0049	0.0002
	石丘（垅岗）	2	390319	−0.5091	−0.0209
	侵蚀谷地地貌	52	2561268	0.8677	0.0356
	构造侵蚀岩溶地貌	2	1167462	−1.6047	−0.0658
起伏度/m	0～4	13	4183395	−1.0071	−0.0594
	4～8	40	4306129	0.0879	0.0052
	8～15	74	5697380	0.4232	0.0249
	15～23	17	2685735	−0.2956	−0.0174
	23～30	2	750479	−1.1607	−0.0684
	30～38	7	294073	1.0289	0.0607
	38～50	0	128414	0	0
	50～220	1	57698	0.7117	0.0419
曲率	<0	70	7431512	0.0997	0.0041
	0	20	3330755	−0.3505	−0.0144
	>0	64	7301960	0.0277	0.0011
距断裂距离/m	0～600	70	6123046	0.2934	0.0194
	600～1200	26	4659019	−0.4237	−0.0279
	1200～1800	20	2742459	−0.1561	−0.0103
	1800～2400	8	1599989	−0.5336	−0.0352
	2400～3000	5	1028561	−0.5617	−0.0371
	>3000	25	1911080	0.4281	0.0282
距河流距离/m	0～600	57	3404716	0.6748	0.0445
	600～1200	32	2816455	0.2872	0.0189
	1200～1800	21	2280631	0.0771	0.0051
	1800～2400	14	1898512	−0.1451	−0.0096
	2400～3000	6	1564553	−0.7989	−0.0528
	>3000	24	6099326	−0.7731	−0.0511

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4.2 加权信息量模型评价结果

根据加权信息量法构建模型，对研究区地质灾害及其背景因素和影响因素的相对重要性进行分析，依据所选取的评价因子，按照专家法对选取的评价因子根据式（3）构建判断矩阵计算出各评价因子的权重值，根据式（5）求出每个判断矩阵的一致性指标CI，并通过式（6）进行一致性检验（表4），各评价因子的权重值与各评价因子分类分级的信息量值根据式（9）得出加权信息量值（表3）。

表 4 评价因子分类分级判断矩阵及其权重

Table 4. Judgment matrix and weight of classification levels for evaluation factors

评价因子	1	2	3	4	5	6	7	8	9	权重	CI/CR
工程岩组	1	2	4	2	6	8	4	4	6	0.31	0.003 0.002
坡度	1/2	1	2	1	3	4	2	2	3	0.155
坡向	1/4	1/2	1	1/2	2	2	1	1	2	0.083
高程	1/2	1	2	1	3	4	2	2	3	0.155
起伏度	1/6	1/3	1/2	1/3	1	1	1/2	1/2	1	0.046
曲率	1/8	1/4	1/2	1/4	1	1	1/2	1/2	1	0.041
距断裂距离	1/4	1/2	1	1/2	2	2	1	1	2	0.082
距河流距离	1/4	1/2	1	1/2	2	2	1	1	2	0.082
地貌类型	1/6	1/3	1/2	1/3	1	1	1/2	1/2	1	0.046

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4.3 信息量-逻辑回归耦合模型评价结果

根据研究区已有的154个崩滑点，并随机选取等量的非崩滑点，共计有308个独立属性样本。将全部样本点依次赋予相应评价因子的信息量值，导入SPSS 25软件进行二项逻辑回归分析（表5），各评价因子的信息量值作为自变量，是否发生地质灾害作为因变量（1和0代表崩滑点和非崩滑点）。

表 5 逻辑回归分析结果

Table 5. Results of logistic regression analysis

评价因子	B	S.E	Wals	df	sig
工程岩组	0.698	0.261	7.142	1	0.002
坡度	1.331	0.513	6.721	1	0.000
坡向	0.761	0.862	0.780	1	0.007
高程	0.309	0.246	1.570	1	0.002
地貌类型	0.171	0.421	0.165	1	0.006
起伏度	0.641	0.304	4.455	1	0.005
曲率	1.523	0.907	2.820	1	0.003
距断裂距离	0.528	0.365	2.090	1	0.004
距河流距离	0.458	0.264	3.001	1	0.000
常量	−0.165	0.142	1.336	1	0.005
注：B为回归系数，S.E为标准误，wals为卡方值，df为自由度，sig为显著性。

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在逻辑回归分析结果中，sig值越小，代表评价因子的显著性越高，表（3）中sig小于0.05，说明9个因子均有统计意义。基于模型分析结果中的各因子系数值根据公式（7）得逻辑回归公式如下：

{\begin{aligned} \begin{aligned} Z & = - 0.165 + 0.698 x_{1} + 0.031 x_{2} + 0.761 x_{3} + 0.309 x_{4} \\ + 0.171 x_{5} + 0.641 x_{6} + 1.523 x_{7} + 0.528 x_{8} + 0.458 x_{9} \end{aligned} \\ P = \frac{1}{1 + e^{- z}} \end{aligned}

(11)

式中：x₁～x₉分别为地层岩组、坡度、坡向、高程、地貌类型、起伏度、曲率、距断裂距离、距河流距离的信息量值；运用ArcGIS的栅格计算器功能将z值代入式（8）得到崩滑灾害发生的概率p。

信息量模型根据信息量法求出各评价因子分类分级的信息量，然后进行叠加分析，加权信息量模型根据表（4）得出的各评价因子的权重值，结合公式（9）求出各分类分级的加权信息量值（表3）。信息量-逻辑回归耦合模型根据公式（11）所求出的概率值构建模型，将结果进行重分类处理，并利用自然断点法将3种评价模型结果划分为非、低、中和高4个等级（图3）。

图 3 崩滑易发性评价结果

Figure 3. Landslide susceptibility evaluation results

下载: 全尺寸图片幻灯片

4.4 易发性评价结果精度检验

为进一步验证3种评价模型分区结果的精度，本文采用ROC（receiver operating characteristic）曲线进行精度检验。ROC曲线又称接收者工作特征曲线，其横轴特异性代表易发性面积百分比累积量，纵轴敏感度代表崩滑地质灾害点数百分比累积量。ROC曲线与坐标轴围成的面积用AUC值来表示，其线下的面积大小表示预测成功率，值越大准确率越高，模型的预测效果越好^[28]。3种评价模型ROC曲线中AUC值分别为0.757，0.723，0.852（图4），加权信息量模型的精度最低，其原因是在采用层次分析法得出评价因子权重时，依据专家打分法构建判断矩阵时主观性因素较大，导致权重综合时降低了准确性，信息量-逻辑回归耦合模型的精度最高，其模型构建主要依据样本点与信息量法中分类分级信息量值进行连接，其精度与所构建的样本点存在紧密的联系，样本点统计规律越明显预测效果越好。

图 4 ROC曲线

Figure 4. ROC curve

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4.5 评价结果分析

根据所得出的评价结果，利用ArcGIS自然断点法将其划分为非、低、中和高4个等级，并将各易发性等级之间的面积（分级比）进行统计（表6），根据3种模型精度评价结果，信息量-逻辑回归耦合模型精度最高，其非-高易发区崩滑面积（分级比）分别为771.1 km²（25.55%）、836.6 km²（27.73%）、864.36 km²（28.64%）和545.94 km²（18.08%）。

表 6 崩滑易发性等级分布预测结果

Table 6. Prediction results of landslide susceptibility grade distribution

易发性等级	信息量模型			加权信息量模型			信息量-逻辑回归耦合模型
易发性等级	分级比/%	崩滑比/%	分级面积/km²	分级比/%	崩滑比/%	分级面积/km²	分级比/%	崩滑比/%	分级面积/km²
非易发区	17.56	5.84	529.96	16.17	7.14	489.03	25.55	6.49	771.1
低易发区	28.27	14.29	853.18	31.80	15.58	959.02	27.73	20.13	836.6
中易发区	32.46	31.17	979.64	33.82	35.06	1020.68	28.64	25.32	864.36
高易发区	21.71	48.70	655.22	18.20	42.21	549.27	18.08	48.05	545.94

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5. 结论

（1）以罗平县为研究对象，选取工程岩组、坡度、坡向、高程、地貌类型、起伏度、曲率、距断裂距离、距河流距离等9个评价因子，进行独立性检验，选取3种评价方法构建易发性评价模型进行对比分析。

（2）通过对评价因子的分类分级处理，计算信息量值和权重值，值较大的因子类分别是：工程岩组中的层状结构坚硬长石石英砂岩岩组、地貌类型中的岩溶中山地貌和侵蚀谷地地貌、坡度主要分布在6°～30°度之间、高程集中在1350～1950 m、起伏度在23 m以下、距断裂距离和距河流距离1800 m之内，信息量值总体为正，对崩滑发育具有促进作用。

（3）根据构建的信息量模型、加权信息量模型和信息量-逻辑回归耦合模型进行对比，通过ROC曲线对3种模型的精度检验，其AUC值分别为0.757，0.723，0.852，模型的精度均大于0.7。结合崩滑点分布图，信息量-逻辑回归耦合模型评价与灾点分布情况相符合，可为快速建立评价指标体系和区域崩滑易发性提供参考依据。

图 1 广义滑坡分类^{[3 − 5]}

Figure 1. Classification of landslides^{[3 − 5]}

下载: 全尺寸图片幻灯片

图 2 海底滑坡浊流链特征^{[4,39 − 40, 98]}

Figure 2. Chain characteristics of submarine landslide-debris flow- turbidity current^{[4,39 − 40, 98]}

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图 3 泥质碎屑流和砂质碎屑流的运动模式^[94]

Figure 3. Patterns of clay debris flow and sandy debris flow^[94]

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图 4 高密度浊流（超临界基底层）体动力学特征

Figure 4. Dynamics characteristics of high-density turbidity current (supercritical base layer)

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图 5 基底动力侵蚀颗粒力学模型

Figure 5. Modelling of forces acting on an eroding particle

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图 6 颗粒动力侵蚀基底模型

Figure 6. A ball particle causing erosion of a surface

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图 7 浊流流线分类^[51]

Figure 7. Streamline classification of the turbid currents^[51]

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0. 引言
1. 研究区概况及数据来源
1.1 研究区概况
1.2 数据来源
2. 研究方法
2.1 信息量模型
2.2 层次分析模型
2.3 逻辑回归模型
2.4 加权信息量模型
2.5 信息量-逻辑回归耦合模型
3. 评价因子选取分级
3.1 评价因子选取分级
3.2 评价因子共线性诊断
3.3 评价因子相关性分析
4. 易发性评价结果
4.1 信息量模型评价结果
4.2 加权信息量模型评价结果
4.3 信息量-逻辑回归耦合模型评价结果
4.4 易发性评价结果精度检验
4.5 评价结果分析
5. 结论

0. 引言
1. 研究区概况及数据来源
1.1 研究区概况
1.2 数据来源
2. 研究方法
2.1 信息量模型
2.2 层次分析模型
2.3 逻辑回归模型
2.4 加权信息量模型
2.5 信息量-逻辑回归耦合模型
3. 评价因子选取分级
3.1 评价因子选取分级
3.2 评价因子共线性诊断
3.3 评价因子相关性分析
4. 易发性评价结果
4.1 信息量模型评价结果
4.2 加权信息量模型评价结果
4.3 信息量-逻辑回归耦合模型评价结果
4.4 易发性评价结果精度检验
4.5 评价结果分析
5. 结论

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海底滑坡动力侵蚀机理研究：回顾与展望

作者简介: 殷跃平（1960—），男，贵州独山人，中国工程院院士，研究员，从事地质灾害防治与研究工作。E-mail：yyueping@mail.cgs.gov.cn

通讯作者: 王文沛（1985—），男，江苏扬中人，土木工程专业，博士，正高级工程师，主要从事地质灾害防治工作。E-mail：jcywangwenpei@mail.cgs.gov.cn

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