滑坡样本缺失下易发性规律和潜在滑坡识别的易发性–InSAR多源信息法

doi:10.3724/1000-6915.jrme.2024.0449

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摘要滑坡样本数据是构建易发性模型的基础，而滑坡样本缺失会影响易发性建模和预测结果的准确性。为了揭示滑坡样本缺失下易发性规律并提出潜在滑坡识别方法，以江西省寻乌县为例，基于野外地质调查的滑坡编录数据(假设为理想工况)，通过随机均匀选取并剔除10%，20%，30%，40%，50%比例和研究区南部滑坡样本模拟不同滑坡样本缺失工况及聚集性缺失工况，分别选取随机森林和支持向量机模型构建不同滑坡样本缺失工况易发性模型，分析滑坡样本缺失对易发性预测结果的影响规律。同时，结合SBAS-InSAR技术和易发性预测结果构建易发性–InSAR多源信息法，进行滑坡样本缺失地区潜在滑坡识别。最后，在滑坡样本聚集性缺失工况的基础上，将识别的潜在滑坡作为扩充样本构建滑坡易发性模型，并进行精度评价。结果表明：(1) 滑坡样本缺失的比例越大，滑坡易发性评价结果的不确定性越强，且聚集性缺失工况下滑坡易发性预测精度下降明显；(2) 易发性–InSAR多源信息法可以准确识别潜在滑坡，为滑坡样本缺失地区扩充滑坡样本提供有效方法；(3) 相较于滑坡样本聚集性缺失的工况，滑坡样本扩充工况下的滑坡易发性结果具有更高的精度和更低的不确定性，表明潜在滑坡识别可有效改善滑坡聚集性缺失这一严肃问题。

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	黄发明1
	2
	吴敦筱1
	常志璐1
	3
	陈茜1
	陶杰1
	蒋水华1
	周创兵1

关键词 ：边坡工程, 滑坡易发性, 不完整滑坡编录, 机器学习, SBAS-InSAR技术, 潜在滑坡识别

Abstract：Landslide sample data is the basis of susceptibility modeling construction，the lack of landslide samples will affect the accuracy of susceptibility modeling and prediction results. In order to reveal the susceptibility law under the different landslide samples missing conditions and propose a potential landslide identification method，the Xunwu County in Jiangxi Province is selected as the case study. The landslide catalog data by field geological survey is assumed as the basic ideal landslide samples condition. And the different landslide sample missing conditions are determined by randomly and evenly selecting and eliminating landslide samples with different proportions of 10%，20%，30%，40% and 50%. Meanwhile，the landslide sample aggregation missing condition is determined by eliminating the landslide samples in the southern part of the study area. Then，the random forest and support vector machine models are selected to construct landslide susceptibility models under different landslide sample missing conditions，and the effect of landslide sample missing conditions on landslide susceptibility prediction is analyzed. Additionally，the Susceptibility-InSAR method has been proposed by combining SBAS-InSAR technology and landslide susceptibility prediction results to identify potential landslides. Finally，based on the landslide sample aggregation missing condition，the potential landslides are used as expanded landslide samples to reconstruct the landslide susceptibility model and to evaluate the performance accuracy. The results show that：(1) the larger the proportion of landslide sample missing data，the stronger the uncertainty of the landslide susceptibility prediction results，and the landslide susceptibility prediction accuracy in landslide sample aggregation missing condition decreases significantly. (2) The Susceptibility-InSAR multi-source information method can accurately identify potential landslides，providing an effective method for expanding landslide samples in areas with missing landslide data. (3) Compared with the landslide sample aggregation missing condition，the landslide susceptibility results in the condition of expanding landslide samples have higher precision and lower uncertainty，suggesting that potential landslide identification can effectively mitigate the significant issue of landslide sample aggregation missing condition.

Key words： slope engineering landslide susceptibility prediction incomplete landslide inventory machine learning SBAS-InSAR technology potential landslide identification

引用本文:

黄发明1，2，吴敦筱1，常志璐1，3，陈茜1，陶杰1，蒋水华1，周创兵1. 滑坡样本缺失下易发性规律和潜在滑坡识别的易发性–InSAR多源信息法[J]. 岩石力学与工程学报, 2025, 44(3): 584-601.
HUANG Faming1，2，WU Dunxiao1，CHANG Zhilu1，3，CHEN Xi1，TAO Jie1，JIANG Shuihua1，ZHOU Chuangbing1. Landslide susceptibility law under the absence of landslide sample and the susceptibility-InSAR multi-source information method considering the potential landslide identification. , 2025, 44(3): 584-601.

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https://rockmech.whrsm.ac.cn/CN/10.3724/1000-6915.jrme.2024.0449 或 https://rockmech.whrsm.ac.cn/CN/Y2025/V44/I3/584

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