Abstract This study presents a novel Bayesian framework for statistical calibration of spatially distributed physical-based landslide prediction models. The calibration process is formulated in a statistical setting with the model parameters simulated as spatially variable with random fields and the model calibration defined within the Bayesian framework. The implementation of such calibration process is challenging due to large numbers of calibration parameters and high-dimensional likelihood functions, which are central in establishing a relation between observations and the corresponding model predictions. The former challenge was resolved by reformulating the Bayesian updating problem as an equivalent reliability problem and solving it with efficient reliability methods. The latter challenge was resolved by developing novel lower-dimensional approximate likelihood formulations, suitable for the interpretation of landslide initiation zones, based on the Approximate Bayesian Computation method. The novelties of the proposed approach stem from describing landslide model parameters as spatially variable, development of a statistical framework to calibrate landslide prediction models, and introduction of approximate likelihood formulations.

中文翻译：

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用于校准空间分布的基于物理的滑坡预测模型的新型贝叶斯框架。

摘要 本研究提出了一种新的贝叶斯框架，用于空间分布的基于物理的滑坡预测模型的统计校准。校准过程是在统计设置中制定的，模型参数模拟为具有随机场的空间变量，模型校准在贝叶斯框架内定义。由于大量的校准参数和高维似然函数，这种校准过程的实施具有挑战性，这对于建立观测值和相应模型预测之间的关系至关重要。前一个挑战是通过将贝叶斯更新问题重新表述为等效可靠性问题并使用有效的可靠性方法解决它来解决的。通过基于近似贝叶斯计算方法开发适用于滑坡起始区解释的新型低维近似似然公式，解决了后一个挑战。所提出的方法的新颖之处在于将滑坡模型参数描述为空间变量、开发用于校准滑坡预测模型的统计框架以及近似似然公式的引入。