Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering acti...Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.展开更多
碳捕集、利用和封存(Carbon Capture,Utilization and Storage)已经成为减少大气中二氧化碳的一种有效方法,但大量的二氧化碳注入地下可能会引起地表发生变形.为了探究二氧化碳注入后注采区的地表变化情况,本文基于45景Sentinel-1A升轨...碳捕集、利用和封存(Carbon Capture,Utilization and Storage)已经成为减少大气中二氧化碳的一种有效方法,但大量的二氧化碳注入地下可能会引起地表发生变形.为了探究二氧化碳注入后注采区的地表变化情况,本文基于45景Sentinel-1A升轨影像,运用SBAS-InSAR技术对国内某CO_(2)陆地埋存实验区域进行为期两年半形变监测工作,并构建了一种适用于小区域顾及GNSS的大气延迟改正模型.根据结果显示,本文提出的大气改正模型可以有效削减干涉图中的对流层延迟误差.根据InSAR结果显示,在注气过程中地表沿卫星视线方向靠近卫星,即地表发生隆起现象.通过提取注气井附近的形变时间序列,转换到垂直方向与GNSS数据对比,发现在注气之后,地表先隆起,几个月后开始逐渐回落.综合分析来看,结合GNSS与InSAR技术可以观测到该地区地表微小形变信息,GNSS监测站不仅可以用于校正InSAR干涉图中的大气延迟误差,还可以用于验证InSAR监测结果.展开更多
基金supported by the National Key R&D Program of China(Grant No.2023YFC3007201)the National Natural Science Foundation of China(Grant No.42377161)the Opening Fund of Key Laboratory of Geological Survey and Evaluation of Ministry of Education(Grant No.GLAB 2024ZR03).
文摘Landslide susceptibility mapping(LSM)is an essential tool for mitigating the escalating global risk of landslides.However,challenges such as the heterogeneity of different landslide triggers,extensive engineering activities exacerbated reactivation,and the interpretability of data-driven models have hindered the practical application of LSM.This work proposes a novel framework for enhancing LSM considering different triggers for accumulation and rock landslides,leveraging interpretable machine learning and Multi-temporal Interferometric Synthetic Aperture Radar(MT-InSAR)technology.Initially,a refined fieldinvestigation was conducted to delineate the accumulation and rock area according to landslide types,leading to the identificationof relevant contributing factors.Deformation along the slope was then combined with time-series analysis to derive a landslide activity level(AL)index to recognize the likelihood of reactivation or dormancy.The SHapley Additive exPlanation(SHAP)technique facilitated the interpretation of factors and the identificationof determinants in high susceptibility areas.The results indicate that random forest(RF)outperformed other models in both accumulation and rock areas.Key factors including thickness and weak intercalation were identifiedfor accumulation and rock landslides.The introduction of AL substantially enhanced the predictive capability of the LSM and outperformed models that neglect movement trends or deformation rates with an average ratio of 81.23%in high susceptibility zones.Besides,the fieldvalidation confirmedthat 83.8%of newly identifiedlandslides were correctly upgraded.Given its efficiencyand operational simplicity,the proposed hybrid model opens new avenues for the feasibility of enhancement in LSM at urban settlements worldwide.
文摘碳捕集、利用和封存(Carbon Capture,Utilization and Storage)已经成为减少大气中二氧化碳的一种有效方法,但大量的二氧化碳注入地下可能会引起地表发生变形.为了探究二氧化碳注入后注采区的地表变化情况,本文基于45景Sentinel-1A升轨影像,运用SBAS-InSAR技术对国内某CO_(2)陆地埋存实验区域进行为期两年半形变监测工作,并构建了一种适用于小区域顾及GNSS的大气延迟改正模型.根据结果显示,本文提出的大气改正模型可以有效削减干涉图中的对流层延迟误差.根据InSAR结果显示,在注气过程中地表沿卫星视线方向靠近卫星,即地表发生隆起现象.通过提取注气井附近的形变时间序列,转换到垂直方向与GNSS数据对比,发现在注气之后,地表先隆起,几个月后开始逐渐回落.综合分析来看,结合GNSS与InSAR技术可以观测到该地区地表微小形变信息,GNSS监测站不仅可以用于校正InSAR干涉图中的大气延迟误差,还可以用于验证InSAR监测结果.
