Joint inversion of physical and geochemical parameters in groundwater models by sequential ensemble-based optimal design

Lan, Tian; Shi, Xiaoqing; Jiang, Beilei; Sun, Yuanyuan; Wu, Jichun

doi:10.1007/s00477-018-1521-5

Cited by 10 publications

(4 citation statements)

References 43 publications

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“…Karhunen-Loève (KL) expansion is a technique used in data analysis to represent a data set in a more efficient way by decomposing the data into a set of simpler, uncorrelated functions capturing the most significant variations within the data. In Bayesian inference, KL expansion has been an effective approach to represent the random field and is successfully applied in various fields (e.g., [33][34][35][36][37][38]). Through KL expansion, thousands of spatially distributed hydraulic parameters can be reduced to dozens of quantities, which greatly reduces the number of predicted parameters and thus the uncertainty of the inverse simulation.…”

Section: Introductionmentioning

confidence: 99%

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Xia,

2024

Applied Sciences

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Quantification of the soil hydraulic conductivity is key to the study of water flow and solute transport in unsaturated soils. Rapid advances in measurement technology have provided a large number of observations at different scales, offering unprecedented opportunities and challenges for the estimation of hydraulic parameters. This paper proposes an inverse estimation method for downscaling of observations on coarse scales to estimate hydraulic parameters on high-resolution scales. Due to the significant spatial heterogeneity, the inversion faces the problems of dynamics-based integration of data at different scales, model uncertainty due to hundreds and thousands of parameters, and computational consumption due to the large number of forward simulations. To overcome these problems, this paper uses an efficient Bayesian optimization DREAM(ZS) as an inverse framework, and incorporates an analytical upscaling method and Karhunen–Loève (KL) expansion to infer finer-scale saturated hydraulic conductivity distribution conditioned on coarse-scale measurements. The efficient upscaling method is used to link measurements and hydraulic parameters at different scales, and Karhunen–Loève (KL) expansion is incorporated to greatly reduce the dimension of the parameter to be estimated. To further improve the efficiency of the inversion, a locally one-dimensional (LOD) algorithm is used to solve the multidimensional water flow model at coarse scales. The proposed inverse model is applied in a series of numerical experiments to demonstrate its applicability and effectiveness under different flow boundary conditions, different levels of ratio between coarse- and fine-scale grids, different densities of observation points, and different degrees of statistic heterogeneity of soil mediums.

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Section: Introductionmentioning

confidence: 99%

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Xia,

2024

Applied Sciences

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“…We propose a new procedure for the joint identification of the source location and the release history of a pollutant in an aquifer: the use of an Ensemble Smoother with Multiple Data Assimilation (ES-MDA) in the context of contaminant source identification. The ES-MDA, introduced by Reynolds (2012, 2013a), has been mainly applied to reservoir history matching problems (Emerick and Reynolds, 2013b;Fokker et al, 2016;, but its popularity is growing also in hydrology (Lan et al, 2018;Li et al, 2018Li et al, , 2019Kang et al, 2019;Song et al, 2019;Todaro et al, 2019;Bao et al, 2020). It is an iterative data assimilation method based on the Ensemble Kalman Filter (EnKF), initially proposed by Evensen (1994).…”

Section: Introductionmentioning

confidence: 99%

Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer

Todaro

D’Oria

Tanda

et al. 2021

Journal of Hydrology

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“…Thus, for better management, conservation and restoration of riverine ecosystems, it is essential to accurately characterize the heterogeneous streambed (Cardenas 2015;Jiménez et al 2015). Due to difficulties in measuring the hydraulic conductivity directly, there are increasing interests in applying inverse modeling methods to estimate it from indirect measurements of state variables in groundwater hydrology (Zhu et al 2017;Lan et al 2018;Liao et al 2018;Zha et al 2018). …”

Section: Introductionmentioning

confidence: 99%

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

Zhang

et al. 2018

Groundwater

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Heat tracing methods have been widely employed for subsurface characterization. Nevertheless, there were very few studies regarding the optimal monitoring design for heat tracing in heterogeneous streambeds. In this study, we addressed this issue by proposing an efficient optimal design framework to collect the most informative diurnal temperature signal for Bayesian estimation of streambed hydraulic conductivities. The data worth (DW) was measured by the expected relative entropy between the prior and posterior distributions of the conductivity field. An adaptively refined Gaussian process surrogate was employed to alleviate the computational burden, resulting in at least three orders of magnitude of speed-up. The applicability of the optimal experimental design framework was evaluated by both numerical and sandbox experimental cases. Results showed that the most informative locations centered in the transition zones among the main patterns of the hydraulic conductivity field, while the most informative times centered in a short period after the minimum/maximum temperature appeared. With the fixed number of measurements, extending the calibration period was more beneficial than increasing the monitoring frequency in improving the estimation results. To our best knowledge, this work is the first study on Bayesian monitoring design for streambed characterization with the heat tracing method. The method and results can provide guidance on selecting monitoring strategies under budget-limited conditions.

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Joint inversion of physical and geochemical parameters in groundwater models by sequential ensemble-based optimal design

Cited by 10 publications

References 43 publications

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

High-Resolution Estimation of Soil Saturated Hydraulic Conductivity via Upscaling and Karhunen–Loève Expansion within DREAM(ZS)

Ensemble smoother with multiple data assimilation to simultaneously estimate the source location and the release history of a contaminant spill in an aquifer

Bayesian Monitoring Design for Streambed Heat Tracing: Numerical Simulation and Sandbox Experiments

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