标题： 河流下切对尼泊尔加尔卡地震引发滑坡的影响：使用inlabru的基于像元的易发性模型结果 显示英文标题

标题： Influence of river incision on landslides triggered in Nepal by the Gorkha earthquake: Results from a pixel-based susceptibility model using inlabru

摘要： 本研究提出了一种综合框架，通过滑坡质心分布的通道分析来模拟地震诱发滑坡（EQILs）。 一个关键创新是将归一化通道陡度指数（$k_{sn}$）作为物理上有意义且新颖的协变量，推断山坡侵蚀和河流下切过程。 在空间点过程模型中，$k_{sn}$支持生成具有量化不确定性的滑坡易发性图。 为解决协变量与滑坡观测之间的空间数据错位问题，我们利用inlabru框架，该框架通过基于网格的分解实现一致集成，从而克服与空间错位数据集成相关的挑战。 我们的建模策略明确优先考虑对未见过的地理区域的前瞻性可转移性，前提是可获得解释变量数据。 通过同时模拟滑坡位置和规模，我们发现较高的$k_{sn}$与增加的滑坡易发性密切相关，但与滑坡规模无关。 最佳拟合的贝叶斯模型通过交叉验证进行验证，为在复杂地形中预测地震诱发滑坡提供了一个可扩展且可解释的解决方案。 显示英文摘要

摘要： This study presents a comprehensive framework for modelling earthquake-induced landslides (EQILs) through a channel-based analysis of landslide centroid distributions. A key innovation is the incorporation of the normalised channel steepness index ($k_{sn}$) as a physically meaningful and novel covariate, inferring hillslope erosion and fluvial incision processes. Used within spatial point process models, $k_{sn}$ supports the generation of landslide susceptibility maps with quantified uncertainty. To address spatial data misalignment between covariates and landslide observations, we leverage the inlabru framework, which enables coherent integration through mesh-based disaggregation, thereby overcoming challenges associated with spatially misaligned data integration. Our modelling strategy explicitly prioritises prospective transferability to unseen geographical regions, provided that explanatory variable data are available. By modelling both landslide locations and sizes, we find that elevated $k_{sn}$ is strongly associated with increased landslide susceptibility but not with landslide magnitude. The best-fitting Bayesian model, validated through cross-validation, offers a scalable and interpretable solution for predicting earthquake-induced landslides in complex terrain.