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首页> 外文期刊>Journal of Contaminant Hydrology >An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling
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An adaptive Kriging surrogate method for efficient joint estimation of hydraulic and biochemical parameters in reactive transport modeling

机译：自适应Kriging替代方法在输运模型中有效联合估算水力和生化参数。

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摘要

Groundwater reactive transport models that consider the coupling of hydraulic and biochemical processes are vital tools for predicting the fate of groundwater contaminants and effective groundwater management. The models involve a large number of parameters whose specification greatly affects the model performance. Thus model parameters calibration is crucial to its successful application. The Bayesian inference framework implemented by Markov chain Monte Carlo (MCMC) sampling provides a comprehensive framework to estimate the model parameters. However, its application is hampered by the large computational requirements caused by repeated evaluations of the model in MCMC sampling. This study develops an adaptive Kriging-based MCMC method to overcome the bottleneck of Bayesian inference by replacing the simulation model with a computationally inexpensive Kriging surrogate model. In the adaptive Kriging-based MCMC method, instead of constructing a globally accurate surrogate of the simulation model, we sequentially build a locally accurate surrogate with an iterative refinement to the high probability regions. The performance of the proposed method is demonstrated using a synthetic groundwater reactive transport model for describing sequential Kinetic degradation of Tetrachloroethene (PCE), whose hydraulic and biochemical parameters are jointly estimated. The results suggest that the adaptive Kriging-based MCMC method is able to achieve an accurate Bayesian inference with a hundredfold reduction in the computational cost compared to the conventional MCMC method.
机译：考虑到水力和生化过程耦合的地下水反应性运输模型是预测地下水污染物和有效管理地下水命运的重要工具。这些模型包含大量参数，这些参数的规格会极大地影响模型性能。因此，模型参数校准对其成功应用至关重要。由马尔可夫链蒙特卡洛（MCMC）采样实现的贝叶斯推理框架提供了一个全面的框架来估计模型参数。但是，由于在MCMC采样中对模型进行重复评估而导致的大量计算需求阻碍了其应用。这项研究开发了一种自适应的基于Kriging的MCMC方法，通过用计算成本低廉的Kriging替代模型代替仿真模型来克服贝叶斯推理的瓶颈。在基于自适应Kriging的MCMC方法中，我们没有构建仿真模型的全局精确替代，而是依次构建了局部精确替代，并迭代改进了高概率区域。使用合成地下水反应性运输模型描述四氯乙烯（PCE）的顺序动力学降解，证明了该方法的性能，该方法共同估算了其水力和生化参数。结果表明，与传统的MCMC方法相比，基于自适应Kriging的MCMC方法能够实现准确的贝叶斯推理，并且计算成本降低了一百倍。

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