In this work an integrated methodological and operational framework for diagnosis and calibration of Stratigraphic Forward Models (SFMs) which are typically employed for the characterization of sedimentary basins is presented. Model diagnosis rests on local and global sensitivity analysis tools and leads to quantification of the relative importance of uncertain model parameters on modeling goals of interest. Model calibration is performed in a stochastic framework, leading to estimates of distributions of model parameters (and ensuing spatial distributions of model outputs) conditional on available information. Starting from a considerable number of uncertain model parameters, which is typically associated with SFMs of the kind analyzed, the approach leads to the identification of a reduced set of parameters which are most influential to drive stratigraphic modeling results. Probability distributions of these model parameters conditional on available data are then evaluated through stochastic inverse modeling. To alleviate computational efforts, this step is performed through a combination of a surrogate model constructed through the Polynomial Chaos Expansion approach and a machine learning algorithm for efficient search of the parameter space during model inversion. As a test bed for the workflow, focus is on a realistic synthetic three-dimensional scenario which is modeled through a widely used SFM that enables one to perform three-dimensional numerical simulations of the accumulation of siliciclastic and carbonate sediments across geologic time scales. These results constitute a robust basis upon which further deployment of the approach to industrial field settings can be designed.

在这项工作中，提出了一个用于诊断和校准地层正向模型（SFM）的综合方法和操作框架，该模型通常用于表征沉积盆地。模型诊断基于局部和全局敏感性分析工具，并导致量化不确定模型参数对目标建模目标的相对重要性。在随机框架中执行模型校准，从而根据可用信息来估计模型参数的分布（并确保模型输出的空间分布）。从大量不确定的模型参数开始，这些参数通常与所分析类型的SFM相关联，该方法可以确定一组减少的参数，这些参数对地层建模结果的影响最大。然后，通过随机逆建模来评估以可用数据为条件的这些模型参数的概率分布。为了减轻计算工作量，此步骤是通过将通过多项式混沌扩展方法构造的替代模型与用于在模型反演期间有效搜索参数空间的机器学习算法结合起来执行的。作为工作流的测试床，重点是通过广泛使用的SFM建模的逼真的合成三维场景，该场景可以对整个地质时标进行硅质碎屑和碳酸盐沉积物的堆积进行三维数值模拟。