Study on Development Patterns and Susceptibility Evaluation of Coseismic Landslides within Mountainous Regions Influenced by Strong Earthquakes
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摘要: 强震山区地形陡峭，植被茂盛，使同震滑坡“点多面广”，难以探测，为灾害防控带来困难.滑坡易发性评估能够预测灾害空间分布.但传统评估方法存在数据源有限、数据量化标准不一等问题，难以获取准确的易发性评价结果及难以掌握复杂孕灾环境下滑坡发育特征.鉴于此，通过多源监测数据、空间分析和深度学习方法，分析同震滑坡的发育规律，探究滑坡的地震响应机制，并进行滑坡易发性区划.结果表明：地震通过影响地形地貌的应力场及岩土体结构对地震波的地震响应的作用，使同震滑坡表现不同形式的发灾效应（如锁固段效应、微地形效应和地层倾向效应等）；采用基于卷积神经网络（CNN）和深度神经网络（DNN）的深度学习模型取得了良好的易发性评价结果（AUC值分别为0.901和0.865），CNN模型的预测性能优于DNN模型.两模型精度都较高，均能较为准确识别潜在的滑坡区域；极高和高滑坡易发性区域广泛分布于丹祖沟等13条沟道中，这些沟道在暴雨下更容易发生泥石流.Abstract: Coseismic landslides are imperceptible and widely distributed in the mountainous regions with abundant vegetation influenced by strong earthquakes, which hinders the development of prevention and control for disaster. Landslide susceptibility evaluation can predict the landslide-prone areas. But few studies have obtained the high-precision landslide susceptibility maps, and it was difficult to uncover the patterns of coseismic landslides under the complex geo-environment, because the lack of data and there was no unified standards for quantification of data based on traditional landslide susceptibility evaluation method. Therefore, the multi-source monitoring data, spatial analysis and deep learning method were adopted in this work to find out the development patterns, seismic response mechanism of landslide and to obtain landslide susceptibility maps. The main conclusions are as follows: the seismic response mechanism of landslide was proposed based on the seismic response analysis of landform and rock structure. The CNN (convolutional neural network) and DNN (deep neural network) achieved the relatively good area under the curve (AUC) value (AUC were 0.901 and 0.865, respectively), which could obtain the high-precision landslide susceptibility maps. Extremely high and high susceptibility region of landslides were widely distributed in 13 gullies, such as Danzu gully and so on. And these gullies were more prone to debris flow under rainstorm.
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Key words:
- development pattern /
- deep learning /
- landslide susceptibility /
- disaster effect /
- hazard geology
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图 2 九寨沟同震滑坡孕灾环境因素
Fig. 2. Geo-environmntal factors of coseismic landslides in Jiuzhaigou
图 3 本文采用的深度神经网络预测模型
Fig. 3. Predication model based on Deep Neural Networks in this work
图 4 本文采用的卷积神经网络预测模型
Fig. 4. Predication model based on convolutional neural network in this work
图 5 熊猫海和九寨天堂附近同震滑坡遥感影像解译实例；
a、e为震前遥感影像；b、c、f和g为震后遥感影像
Fig. 5. Coseismic landslides interpretation based on remote sensing images near Panda Lake and Jiuzhai Paradise
图 8 滑坡孕灾环境因素敏感性分析
Fig. 8. Sensitivity analysis of geo-environmental factors for coseismic landslides
图 9 滑坡与孕灾环境因素的空间关系
Fig. 9. Relationship of geo-environmental factors and coseismic landslides
图 11 九寨沟地震滑坡易发性评价结果
a、b分别为基于卷积神经网络和深度神经网络的滑坡易发性评价结果
Fig. 11. Coseismic landslide susceptibility mapping in Jiuzhaigou
图 12 基于卷积神经网络和深度神经网络的滑坡易发性评价模型ROC曲线
Fig. 12. ROC curve of evaluation models of landslide susceptibility based on convolutional neural network (CNN) and deep neural network (DNN)
表 1 遥感影像和地理数据
Table 1. Remote sensing image and geographic information data
数据类型 数据来源 日期 分辨率 遥感影像 Sentinel-2A 20170729 10 m 20170907 10 m Google image 20151207 0.3 m 20170814 0.3 m 地震数据 美国地质调查局 20170808 矢量数据 中国地震局 20170808 矢量数据 地质数据 中国地质调查局 震前 1∶200 000 地形数据 美国航空航天局 20110213 12.5 
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表 2 同震滑坡面积占各易发性区域的百分比
Table 2. Percentage of coseismic landslides in the susceptibility region
DNN CNN 滑坡占极高易发区(%) 64.71 77.33 滑坡占高易发区(%) 20.26 11.45 滑坡占中等易发区(%) 6.79 5.15 滑坡占低易发区(%) 3.70 2.74 滑坡占稳定区(%) 4.54 3.33
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表 3 2017年九寨沟地震诱发滑坡相关研究对比结果
Table 3. Comparison of relevant studies on landslides triggered by the 2017 Jiuzhaigou earthquake
研究区面积（km2） 同震滑坡面积（km2） 滑坡数量（个） 滑坡最大面积（104m2） 最小面积（m2） 遥感影像数据源 戴岚欣等(2017)、Fan et al. (2018) 840 8.11 1 883 23.1 9.7 SPOT5（2.5 m）、UAV（0.2 m）、GF-1、GF-2（1 m） 许冲等(2018) / 9.6 4 800 / / GF-1与GF-2（0.8~2 m）、Google image（0.5 m） Wang et al. (2018) 651.3 11.8 2 212 20.9 10 Google image（1 m）、GF-2（4 m）、Sentinel-2A（10 m）、UAV Wu et al. (2018) 1 275 11.8 2 122 23.1 9.7 Google image（1 m）、GF-2（4 m）、Sentinel-2A（10 m）、UAV Tian et al. (2019) 434 9.64 4 834 23.6 7.8 Google image（0.5 m） Yi et al. (2020) 546.85 / 681 20.0 80 Sentinel-2A（10 m）、GF-1（2 m）、GF-2（1 m） Ling et al. (2021) 1 840 14.1 5 633 23.9 15 Google image（0.5 m）、GF-2（1 m）、UAV（0.2 m）、Sentinel-2A（10 m） Wang and Mao (2022) 938.9 9.45 5 431 24.3 6 Google image（0.3 m）、GF-1、GF-2（2 m）、Sentinel-2A（10 m）、Planet image（3m） 本研究 1 330 10.56 5 487 24.3 5.5 Google image（0.3 m）、Sentinel-2A（10 m） 注：UAV为Unmanned Aerial Vehicle images无人机影像; GF⁃1和GF⁃2分别为高分1号和2号影像.
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