Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation
Hierarchical Bayesian model averaging for hydrostratigraphic modeling: Uncertainty segregation and comparative evaluation
复制标题
水文地层建模的分层贝叶斯模型平均：不确定性分离和比较评估
DOI：
10.1002/wrcr.20428
复制
发表时间：
2013
影响因子：
5.4
通讯作者：
A. Elshall
中科院分区：
文献类型：
--
作者：
F. Tsai;A. Elshall
分析师经常面对每个不确定模型组件的竞争命题。我们如何判断我们从众多可能的命题中为一个不确定的模型组件选择了一个正确的命题？我们介绍了层次贝叶斯模型平均（HBMA）方法作为一个多模型框架的不确定性分析。HBMA允许通过形成BMA树的BMA模型的层次结构来隔离、优先化和评估不同的不确定性来源及其相应的竞争主张。我们应用HBMA对路易斯安那州巴吞鲁日含水层-断层系统重建的水文地层结构进行不确定性分析。由于模型数据、结构和参数的不确定性，产生多种可能的水文地层模型并将其校准为基础模型。该研究考虑了四个不确定性来源。关于数据的不确定性，研究考虑了两个校准数据集。关于模型结构，研究考虑了三种不同的变差函数模型，两个地质平稳性假设和两个故障概念化。基础模型是按照组合设计产生的，以允许不确定性隔离。因此，这四个不确定模型组件及其相应的竞争模型命题产生了24个基础模型。结果表明，系统解剖的不确定性模型组件沿着与其相应的竞争命题允许检测鲁棒模型命题和不确定性的主要来源。
Analysts are often faced with competing propositions for each uncertain model component. How can we judge that we select a correct proposition(s) for an uncertain model component out of numerous possible propositions? We introduce the hierarchical Bayesian model averaging (HBMA) method as a multimodel framework for uncertainty analysis. The HBMA allows for segregating, prioritizing, and evaluating different sources of uncertainty and their corresponding competing propositions through a hierarchy of BMA models that forms a BMA tree. We apply the HBMA to conduct uncertainty analysis on the reconstructed hydrostratigraphic architectures of the Baton Rouge aquifer‐fault system, Louisiana. Due to uncertainty in model data, structure, and parameters, multiple possible hydrostratigraphic models are produced and calibrated as base models. The study considers four sources of uncertainty. With respect to data uncertainty, the study considers two calibration data sets. With respect to model structure, the study considers three different variogram models, two geological stationarity assumptions and two fault conceptualizations. The base models are produced following a combinatorial design to allow for uncertainty segregation. Thus, these four uncertain model components with their corresponding competing model propositions result in 24 base models. The results show that the systematic dissection of the uncertain model components along with their corresponding competing propositions allows for detecting the robust model propositions and the major sources of uncertainty.