Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality
Optimization and uncertainty assessment of strongly nonlinear groundwater models with high parameter dimensionality
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DOI：
10.1029/2009wr008584
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发表时间：
2010-10-12
影响因子：
5.4
通讯作者：
Kang, Qinjun
中科院分区：
文献类型：
--
作者：
Keating, Elizabeth H.;Doherty, John;Kang, Qinjun
高度参数化和CPU密集型地下水模型正越来越多地用于了解和预测通过含水层的流量和输运。尽管它们经常使用，这些模型对参数估计和预测不确定性分析算法，特别是通常需要非常大量的前向运行的全局方法提出了重大挑战。在这里，我们提出了一个一般的方法参数估计和不确定性分析，可以在这些情况下使用。我们提出的方法包括提取一个代理模型，模仿一个完整的过程模型的关键特征，然后测试和实施一个务实的不确定性分析技术，称为零空间蒙特卡罗（NSMC），合并的优势，基于梯度的搜索和参数降维。作为代理模型分析的一部分，NSMC的结果进行了比较，与一个正式的贝叶斯方法使用的基本进化自适应大都会（DREAM）算法。这样的比较以前从未完成过，特别是在高参数维度的情况下。尽管反问题的高度非线性性质，存在多个局部极小值，以及相对较大的参数维数，这两种方法表现良好，结果相互比较有利。从代理模型分析中获得的经验，然后转移到校准全高度参数化和CPU密集型地下水模型，并探讨该模型的预测预测的不确定性。这里提出的方法通常适用于任何高度参数化和CPU密集型的环境模型，其中有效的方法，如NSMC提供了进行预测不确定性分析的唯一实用手段。
Highly parameterized and CPU-intensive groundwater models are increasingly being used to understand and predict flow and transport through aquifers. Despite their frequent use, these models pose significant challenges for parameter estimation and predictive uncertainty analysis algorithms, particularly global methods which usually require very large numbers of forward runs. Here we present a general methodology for parameter estimation and uncertainty analysis that can be utilized in these situations. Our proposed method includes extraction of a surrogate model that mimics key characteristics of a full process model, followed by testing and implementation of a pragmatic uncertainty analysis technique, called null-space Monte Carlo (NSMC), that merges the strengths of gradient-based search and parameter dimensionality reduction. As part of the surrogate model analysis, the results of NSMC are compared with a formal Bayesian approach using the DiffeRential Evolution Adaptive Metropolis (DREAM) algorithm. Such a comparison has never been accomplished before, especially in the context of high parameter dimensionality. Despite the highly nonlinear nature of the inverse problem, the existence of multiple local minima, and the relatively large parameter dimensionality, both methods performed well and results compare favorably with each other. Experiences gained from the surrogate model analysis are then transferred to calibrate the full highly parameterized and CPU intensive groundwater model and to explore predictive uncertainty of predictions made by that model. The methodology presented here is generally applicable to any highly parameterized and CPU-intensive environmental model, where efficient methods such as NSMC provide the only practical means for conducting predictive uncertainty analysis.