Regional mapping of landslide susceptibility aims to identify zones of potential instability across geological settings. Given their predictive capabilities, physically based, deterministic models are useful tools for landslide triggering studies at regional scale. However, they rely on detailed input parameters that are rarely available for large areas. To address these limitations, this work proposes a computational framework to incorporate the spatial uncertainty of input data into physically based, landslide hazard zonation models through the use of regional scale random fields (RSRF). For this purpose, input parameters are treated as spatially correlated random variables with assigned statistical attributes, while a vectorization strategy is used to reduce the computational cost of large-scale stochastic analyses. Deterministic simulations based on a hydro-mechanical model are then performed for multiple Monte Carlo realizations to compute maps of failure probability ( p f ). The methodology was applied to a well-documented series of rainfall-induced shallow landslides in a volcanic site for which field measurements were available to constrain the statistical variability of the hydraulic conductivity and treat this parameter as an RSRF. To analyze the results, four classes of landslide susceptibility characterized by different p f thresholds were used. Such classes were mapped over the study zone and throughout the storm event, allowing a direct comparison with the spatio-temporal evidence of landslide triggering. The results indicate that (i) uncertainty analyses neglecting the role of spatial correlation may lead to non-conservative estimates of landslide susceptibility and (ii) there is an interval of spatial correlation distance that optimizes the performance of the model, thus providing an indirect estimate of the heterogeneity of the site. Such results highlight the benefits of accounting for the uncertainty of the soil properties in regional-scale models and offer a new predictive stochastic framework to assess the implications of future rainfall scenarios over large areas.

中文翻译：

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使用区域尺度随机场的浅层滑坡起始概率建模

滑坡敏感性区域绘图旨在确定地质环境中潜在的不稳定区域。鉴于其预测能力，基于物理的确定性模型是区域尺度滑坡触发研究的有用工具。然而，它们依赖于在大区域很少可用的详细输入参数。为了解决这些限制，这项工作提出了一个计算框架，通过使用区域尺度随机场 (RSRF) 将输入数据的空间不确定性纳入基于物理的滑坡灾害分区模型。为此，输入参数被视为具有指定统计属性的空间相关随机变量，同时使用矢量化策略来降低大规模随机分析的计算成本。然后针对多个蒙特卡罗实现执行基于流体力学模型的确定性模拟，以计算故障概率图 (pf)。该方法应用于火山遗址中一系列有据可查的降雨引起的浅层滑坡，现场测量可用于限制水力传导率的统计可变性并将该参数视为 RSRF。为了分析结果，使用了以不同 pf 阈值为特征的四类滑坡敏感性。这些类别被绘制在研究区和整个风暴事件中，允许与滑坡触发的时空证据进行直接比较。结果表明（i）忽略空间相关作用的不确定性分析可能导致滑坡敏感性的非保守估计，以及（ii）存在优化模型性能的空间相关距离区间，从而提供间接估计站点的异质性。这些结果突出了在区域尺度模型中考虑土壤特性不确定性的好处，并提供了一个新的预测随机框架来评估未来大面积降雨情景的影响。