Probabilistic modeling of shallow landslide initiation using regional scale random fields
Probabilistic modeling of shallow landslide initiation using regional scale random fields
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DOI：
10.1007/s10346-020-01438-y
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发表时间：
2020-06
期刊：
影响因子：
6.7
通讯作者：
J. Lizárraga;G. Buscarnera
中科院分区：
文献类型：
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作者：
J. Lizárraga;G. Buscarnera
滑坡易发性区域制图旨在确定地质背景中潜在的不稳定区域。鉴于其预测能力，基于物理的，确定性模型是有用的工具，在区域尺度上的滑坡触发研究。然而，它们依赖于很少可用于大区域的详细输入参数。为了解决这些局限性，这项工作提出了一个计算框架，将输入数据的空间不确定性纳入物理为基础的，滑坡灾害区划模型，通过使用区域尺度随机场（RSRF）。为此，输入参数被视为空间相关的随机变量与指定的统计属性，而向量化策略是用来减少大规模的随机分析的计算成本。确定性模拟的基础上的流体力学模型，然后进行多个蒙特卡洛实现计算地图的故障概率（PF）。该方法被应用到一个有据可查的一系列的火山网站，现场测量可用于约束的水力传导率的统计变异性和治疗这个参数作为RSRF的火山岩引起的浅层滑坡。为了分析结果，使用了以不同pf阈值为特征的四类滑坡易感性。这些类被映射在研究区和整个风暴事件，允许直接比较与滑坡触发的时空证据。结果表明，（i）不确定性分析忽略了空间相关性的作用，可能会导致非保守估计的滑坡敏感性和（ii）有一个区间的空间相关距离，优化模型的性能，从而提供了一个间接的估计网站的异质性。这样的结果突出了占区域尺度模型中的土壤性质的不确定性的好处，并提供了一个新的预测随机框架，以评估未来降雨情景在大面积的影响。
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 (pf). 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 differentpfthresholds 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.