2014年 MW6.2 鲁甸地震滑坡易发性评估:基于卷积神经网络与随机森林算法的对比

doi:10.3969/j.issn.0253-4967.20240139

地震地质 ›› 2026, Vol. 48 ›› Issue (2): 497-519.DOI: 10.3969/j.issn.0253-4967.20240139

2014年 M_W6.2 鲁甸地震滑坡易发性评估:基于卷积神经网络与随机森林算法的对比

王莞瞳¹^,²^,³⁾(), 马思远¹^,²⁾, 严武建³⁾, 袁仁茂¹^,²⁾^,^*()

¹⁾ 中国地震局地质研究所, 北京 100029
²⁾ 中国地震局地质研究所, 地震与火山灾害重点实验室, 北京 100029
³⁾ 中国地震局兰州地震研究所, 兰州 730000

收稿日期:2025-01-21 修回日期:2025-03-15 出版日期:2026-04-20 发布日期:2026-05-14
通讯作者: * 袁仁茂, 男, 1972年生, 研究员, 主要研究方向为地震地质灾害。E-mail: yuanrenmao@ies.ac.cn。
作者简介:
王莞瞳, 女, 1989年生, 2025年于中国地震局兰州地震研究所获理学硕士学位, 研究方向为地震地质灾害, E-mail: wwt@ies.ac.cn。
基金资助:
国家自然科学基金(42330704); 中国地震局地震科技星火计划攻关项目(XH24044A)

LANDSLIDE SUSCEPTIBILITY ASSESSMENT OF THE 2014 LUDIAN EARTHQUAKE: COMPARATIVE STUDY BASED ON CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST

WANG Wan-tong¹^,²^,³⁾(), MA Si-yuan¹^,²⁾, YAN Wu-jian³⁾, YUAN Ren-mao¹^,²⁾^,^*()

¹⁾ Institute of Geology, China Earthquake Administration, Beijing 100029, China
²⁾ Key Laboratory of Seismic and Volcanic Hazards, Institute of Geology, China Earthquake Administration, Beijing 100029, China
³⁾ Lanzhou Institute of Seismology, China Earthquake Administration, Lanzhou 730000, China

Received:2025-01-21 Revised:2025-03-15 Online:2026-04-20 Published:2026-05-14

摘要/Abstract

摘要：

地震滑坡作为一种破坏性极强的地质灾害, 不仅对灾区居民生命安全构成威胁, 还对当地基础设施和交通网络造成严重破坏。因此, 快速、准确地识别同震滑坡分布并评估其易发性, 对于决策者及时部署应急响应、合理配置资源及最大限度减少人员伤亡和财产损失具有至关重要的意义。文中以2014年 M_W6.2 鲁甸地震为例, 基于卷积神经网络(CNN)构建了AlexNetCBAM滑坡易发性评估模型, 并采用随机森林(RF)模型作为对比工具, 开展了鲁甸震区滑坡易发性评估。研究综合选取了高程、坡度、坡向、地震烈度、断层距离、地层岩性等10个影响因子, 并通过GINI指数定量分析了各影响因子对滑坡发生的贡献程度。结果表明, 相较于地貌特征和地质条件, 地震因素对鲁甸地震滑坡的控制作用最为显著。进一步比较2种模型预测的滑坡敏感性图(LSM), 发现滑坡高易发区的分布与实际滑坡分布情况基本一致, 鲁甸地震极高和高危险区主要集中在2个区域: 1)距震中最远的东南部河谷地区; 2)震中西侧沿EW向从BXF断层至牛栏河的区域。根据定量评价指标可知, AlexNetCBAM模型和RF模型的准确率分别为0.84和0.82, AUC值分别为0.91和0.90, 表明AlexNetCBAM模型在鲁甸地区同震滑坡预测中的表现略优于RF模型。

关键词: 鲁甸M_W6.2地震, 同震滑坡, 卷积神经网络, 随机森林, 易发性评估

Abstract:

Landslides are among the most common geological hazards worldwide, causing severe environmental damage and significant socio-economic losses in affected areas. In China, geological disasters are characterized by wide spatial distribution, high frequency, strong destructiveness, and substantial losses. In particular, landslides triggered by strong earthquakes have profoundly impacted the socio-economic development of southwestern China. Ludian County in Yunnan Province is located within the Sichuan-Yunnan region, where both seismic activity and intense rainfall pose serious hazards. The region’s steep topography, fractured rock masses, abundant precipitation, and active faulting contribute to frequent landslides and debris flows. Therefore, establishing a rapid and accurate landslide susceptibility assessment system is of great importance for improving disaster prevention and risk management and for reducing losses associated with earthquake-induced landslides.
Using the 2014 Ludian earthquake as a case study, a database of coseismic landslides was established through visual interpretation of ultra-high-resolution satellite imagery from the Google Earth platform. Three-dimensional landslide and non-landslide samples were extracted based on multiple influencing factors. A rapid landslide susceptibility assessment model, AlexNetCBAM, was developed based on a convolutional neural network(CNN), and a Random Forest(RF)model was employed for comparison. Model performance was quantitatively evaluated using statistical indicators, including receiver operating characteristic(ROC)curves, area under the curve(AUC), and F1-score, to verify the reliability and applicability of the models in the Ludian area.
Ten influencing factors, including elevation, slope angle, slope aspect, seismic intensity, distance to faults, and lithology, were selected. The Gini index was used to quantify the contribution of each factor to landslide occurrence. The results indicate that distance to faults, seismic intensity, and peak ground acceleration(PGA)exhibit the strongest predictive capability, suggesting that seismic factors exert a dominant control on landslide occurrence in the Ludian earthquake area compared with topographic and geological conditions. Removing highly correlated and low-importance factors reduces redundancy and improves data quality, providing a solid foundation for model development.
A comparison of the landslide susceptibility maps(LSMs)generated by the two models shows that areas of high susceptibility closely correspond to observed landslide distributions. Extremely high and high susceptibility zones are mainly concentrated in two regions: the southeastern valley area far from the epicenter, and the zone extending from the BXF fault to the Niulan River along a west-east direction on the western side of the epicenter. These areas should be prioritized for post-earthquake monitoring, early warning, and emergency response.
Comparative analysis of the ROC curves and related statistical metrics indicates that the prediction accuracies of the AlexNetCBAM and RF models are 0.84 and 0.82, respectively, with corresponding AUC values of 0.91 and 0.90. These results demonstrate that the AlexNetCBAM model slightly outperforms the RF model in predicting coseismic landslides in the Ludian area. Correlation analysis of landslide susceptibility index(LSI)values between the two models reveals a linear relationship of LSICNN=0.82*LSIRF, indicating that the CNN-based model more effectively classifies non-landslide pixels into low-susceptibility zones and landslide pixels into high and very high susceptibility zones. Compared with the traditional RF model, the CNN model exhibits stronger predictive capability, highlighting the adaptability and advantages of deep learning methods in earthquake-induced landslide susceptibility assessment.
This study enables rapid and accurate identification of coseismic landslide-prone areas and provides a scientific basis for emergency response planning, including rescue team deployment and resource allocation, thereby helping to reduce casualties and property losses.

Key words: Ludian M_W6.2 earthquake, coseismic landslide, Convolutional Neural Network, Random Forest, landslide susceptibility assessment

王莞瞳, 马思远, 严武建, 袁仁茂. 2014年 M_W6.2 鲁甸地震滑坡易发性评估:基于卷积神经网络与随机森林算法的对比[J]. 地震地质, 2026, 48(2): 497-519.

WANG Wan-tong, MA Si-yuan, YAN Wu-jian, YUAN Ren-mao. LANDSLIDE SUSCEPTIBILITY ASSESSMENT OF THE 2014 LUDIAN EARTHQUAKE: COMPARATIVE STUDY BASED ON CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST[J]. SEISMOLOGY AND GEOLOGY, 2026, 48(2): 497-519.

图/表 16

图1 a 研究区域与昭通地区位置关系; b 研究区构造背景图

Fig. 1 Map showing the geographical location of study area(a), and the elevation, historical earthquakes, and tectonic setting of study area(b).

图2 研究区地层岩性图

Fig. 2 Lithology distribution of the study area.

图3 鲁甸地震滑坡分布图

Fig. 3 The distribution of coseismic landslides triggered by the 2014 Ludian earthquake.

图4 研究区滑坡分布特征 a 滑坡面积密度; b 滑坡数量密度

Fig. 4 The distribution characteristics of landslide in the study area.

图5 影响因子分布图 a 坡度; b 坡向; c 与河流距离; d 与断层距离; e PGA; f NDVI; g 地震烈度; h 土地利用。数值区间为“下闭上开”形式

Fig. 5 The distribution of the influencing factors in the study area.

图6 AlexNetCBAM模型原理示意图

Fig. 6 Diagram showing the principle of AlexNetCBAM model.

图7 随机森林模型算法示意图

Fig. 7 Schematic diagram of the random forest model.

表1 各影响因子VIF值和GINI重要性值

Table 1 VIF value and GI value of the influencing factors

影响因子	重要性(GINI)	VIF
与断层的距离	0.222	2.856
地震烈度	0.216	3.018
PGA	0.132	1.821
与河流的距离	0.111	3.405
NDVI	0.088	2.273
高程	0.074	3.194
坡度	0.052	1.250
地层岩性	0.039	2.131
坡向	0.037	1.031
土地利用	0.029	1.076

图8 各影响因子相关系数图

Fig. 8 The correlation coefficient matrix for the influencing factors.

图9 滑坡易发性概率图 a AlexNetCBAM模型; b RF模型

Fig. 9 The predicted results of the occurrence probability of landslides based on two different models.

图10 AlexNetCBAM滑坡易发性评估图 a AlexNetCBAM模型; b RF模型

Fig. 10 Map of landslides susceptibility class for two different models using AlexNetCBAM.

图11 按滑坡易发性等级划分的预测面积、 LND、 LAD及滑坡数量统计图 a AlexNetCBAM模型; b RF模型

Fig. 11 The statistical result of the predicted area and LND, LAD, landslide number under the landslides susceptibility class for two different models.

图12 ROC曲线和AUC值

Fig. 12 The ROC curve and AUC value.

表2 2个模型准确率、精确度、召回率和F1-score值

Table 2 Accuracy value, precision value, recall value and F1-score for two different models

模型	TP	TN	FN	FP	准确率	精确度	召回率	F1-score
AlexNetCBAM	2883	2441	726	284	0.841	0.910	0.799	0.851
RF	2941	2307	860	226	0.829	0.929	0.774	0.844

图13 AlexNetCBAM模型和RF模型的滑坡易发性评价结果之间的LSI关系图

Fig. 13 The relationship of LSI between AlexNetCBAM and RF-based landslide susceptibility assessment results.

图14 滑坡发生概率图中局部滑坡分布对比区间分类统一采用“左开右闭”形式, 即下限值不计入本级、上限值计入本级

Fig. 14 Comparison of local landslide distribution in the occurrence probabilistic map.

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2014年 M_W6.2 鲁甸地震滑坡易发性评估:基于卷积神经网络与随机森林算法的对比

LANDSLIDE SUSCEPTIBILITY ASSESSMENT OF THE 2014 LUDIAN EARTHQUAKE: COMPARATIVE STUDY BASED ON CONVOLUTIONAL NEURAL NETWORK AND RANDOM FOREST

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