Accelerating Groundwater Data Assimilation With a Gradient‐Free Active Subspace Method. Issue 12 (1st December 2021)

• Record Type: Journal Article
• Authors: Yan, Hengnian; Hao, Chenyu; Zhang, Jiangjiang; Illman, Walter A.; Lin, Guang; Zeng, Lingzao
• Is Part Of: Water resources research. Volume 57:Issue 12(2021)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2021-12-01
• Subjects: data assimilation -- Gaussian process -- iterative ensemble smoother -- gradient‐free -- active subspace method; Hydrology -- Periodicals; 333.91
• DOI: 10.1029/2021WR029610
• Languages: English
• ISSNs: 0043-1397

Abstract

Abstract: Groundwater models always involve high‐dimensional parameters, which makes computationally tractable data assimilation using surrogate models very challenging. To address this issue, one common practice is to employ dimension reduction (DR) techniques. Nevertheless, traditional DR methods are usually implemented based on prior parameter statistics, that is, without considering the inherent system dynamics. Here, we show that when significant difference in parameter sensitivity exists, further efficiency can be achieved by adopting a supervised DR method, that is, the active subspace (AS) method. To avoid non‐trivial efforts in calculating the gradient information needed in the standard AS method, a cluster‐based gradient‐free AS (GFAS) method is developed in this study. By combining GFAS with Gaussian process regression, a surrogate model for the CPU‐demanding groundwater model can be adaptively constructed to accelerate data assimilation. Furthermore, a compensation scheme is proposed to cope with uncertainty underestimation caused by DR. The developed approach is tested with numerical experiments and field cases, which illustrated that the new approach is more efficient than the previously developed unsupervised ones by incorporating sensitivity information. Although an iterative ensemble smoother is employed in this study, the proposed method can also be used in other data assimilation approaches, such as Markov chain Monte Carlo and ensemble Kalman filter. Key Points: The active subspace method reduces the dimensionality by incorporating sensitivity information A cluster‐based gradient‐free method is proposed for searching the active subspace A compensation scheme is proposed to alleviate uncertainty underestimation