Physically-based avalanche propagation models must still be locally calibrated to provide robust predictions, e.g. in long-term forecasting and subsequent risk assessment. Friction parameters cannot be measured directly and need to be estimated from observations. Rich and diverse data are now increasingly available from test-sites, but for measurements made along flow propagation, potential autocorrelation should be explicitly accounted for. To this aim, this work proposes a comprehensive Bayesian calibration and statistical model selection framework. As a proof of concept, the framework was applied to an avalanche sliding block model with the standard Voellmy friction law and high rate photogrammetric images. An avalanche released at the Lautaret test-site and a synthetic data set based on the avalanche are used to test the approach and to illustrate its benefits. Results demonstrate (1) the efficiency of the proposed calibration scheme, and (2) that including autocorrelation in the statistical modelling definitely improves the accuracy of both parameter estimation and velocity predictions. Our approach could be extended without loss of generality to the calibration of any avalanche dynamics model from any type of measurement stemming from the same avalanche flow.

中文翻译：

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从沿流的自相关测量中对雪崩模型进行贝叶斯校准：应用于从摄影测量图像中提取的速度

基于物理的雪崩传播模型仍必须进行局部校准，以提供可靠的预测，例如在长期预测和随后的风险评估中。摩擦参数不能直接测量，需要从观察中估计。现在越来越多地从测试站点获得丰富多样的数据，但对于沿流动传播进行的测量，应明确考虑潜在的自相关。为此，这项工作提出了一个全面的贝叶斯校准和统计模型选择框架。作为概念验证，该框架被应用于具有标准 Voellmy 摩擦定律和高速摄影测量图像的雪崩滑块模型。Lautaret 测试站点发布的雪崩和基于雪崩的合成数据集用于测试该方法并说明其优势。结果表明 (1) 所提出的校准方案的效率，以及 (2) 在统计建模中包含自相关肯定会提高参数估计和速度预测的准确性。我们的方法可以在不失通用性的情况下扩展到从源自相同雪崩流的任何类型的测量中校准任何雪崩动力学模型。