Article
Engineering, Civil
Huiyang Qiu, Rui Hu, Ning Luo, Walter A. Illman, Xiaolan Hou
Summary: This paper compares the performances of travel-time based inversion (TTI) and geostatistical inversion (GI) approaches in hydraulic tomography (HT). The results show that TTI can better reveal the structural features of high-diffusivity zones and requires less data for inverse modeling. On the other hand, GI can estimate parameters throughout the simulation domain, better characterize low-diffusivity zones, and generate the best estimated tomogram for accurate drawdown predictions.
JOURNAL OF HYDROLOGY
(2023)
Article
Water Resources
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
Summary: This article presents a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE) to compress bathymetry and flow velocity information, allowing for fast solving of bathymetry inverse problems. By constructing ROMs on a nonlinear manifold and employing a Hierarchical Bayesian setting, variational inference and efficient uncertainty quantification can be achieved using a small number of ROM runs.
ADVANCES IN WATER RESOURCES
(2022)
Article
Engineering, Civil
Zhanfeng Zhao, Walter A. Illman
Summary: Hydraulic tomography has been developed as a robust technique for characterizing subsurface heterogeneity, but geostatistically-based inversion approaches may lack geological features when observation data is sparse. In this study, local hydrostratigraphic layers of glaciofluvial deposits were derived from corrected pressure logs collected during HPT surveys, and site-specific geological models were developed and calibrated to predict drawdown data of multiple pumping tests. The calibrated geological models outperformed geostatistical inversion approaches in predicting independent pumping tests, demonstrating the usefulness of integrating stratigraphic information derived from HPT logs for capturing sharp boundaries in hydraulic conductivity fields.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Yang Song, Rui Hu, Quan Liu, Huiyang Qiu, Xiaolan Hou, Junjie Qi, Bernard Konadu-Amoah
Summary: This study aimed to evaluate the utility of multiple inversion techniques on aquifer heterogeneity characterization. A series of warm water injection tests were simulated in a fluvial aquifer analogue outcrop, and the calculated head and temperature datasets were used with the four above-mentioned inversion methods to reveal the aquifer heterogeneity. The results showed that thermal tracer tomography, hydraulic travel time, and attenuation tomography accurately characterized the high permeability zones within the well area, while the geological statistical method depicted the overall distribution of K values for a larger area. By comparing and combining the individual inversion results, the scientific and economic complementarity can be studied, and valuable advice for the choice of different inversion methods can be recommended for future practices.
Article
Engineering, Civil
Zhanfeng Zhao, Ning Luo, Walter A. Illman
Summary: Hydraulic profiling tool (HPT) and hydraulic tomography (HT) are promising techniques for high-resolution characterization of surficial aquifer systems. HPT surveys provide rapid one-dimensional vertical profiles, while HT can estimate three-dimensional distributions of hydraulic parameters. This study evaluated the benefits of incorporating HPT profiles into HT for characterizing a highly heterogeneous glaciofluvial multi-aquifer-aquitard system. Results showed that combining HPT and HT improved the capture of spatial heterogeneity and refinement of layer boundaries, demonstrating the potential of these techniques for subsurface characterization.
JOURNAL OF HYDROLOGY
(2023)
Article
Environmental Sciences
Yue Zhao, Jian Luo
Summary: This research proposes a dimension-reduced quasi-Newton method within the geostatistical framework to lower computational costs by updating the approximate Jacobian of the forward model. The efficiency and validity of this approach are tested through numerical experiments, demonstrating its effectiveness in providing satisfactory inverse results.
WATER RESOURCES RESEARCH
(2021)
Article
Engineering, Geological
Shucai Li, Xintong Wang, Zhenhao Xu, Deqiang Mao, Dongdong Pan
Summary: Hydraulic tomography combined with the successive linear estimator algorithm can effectively describe subsurface hydraulic heterogeneity, and responses from pumping tests can improve the reliability of spatial distribution. Transient hydraulic tomography (THT) provides better indications of heterogeneity in karst models than steady-state hydraulic tomography (SSHT), and reliable geological data can enhance the accuracy of estimated hydraulic parameters.
ENGINEERING GEOLOGY
(2021)
Article
Water Resources
Behzad Pouladi, Niklas Linde, Laurent Longuevergne, Olivier Bour
Summary: Hydraulic tomography is an advanced method for inferring hydraulic conductivity fields using head data, with flux data providing better resolution in similar signal-to-noise ratios. The quality of estimated fields is similar when considering a high number of observation points, and joint inversion does not offer advantages over individual inversions with the same number of observations. Joint inversion performs better than individual inversions when considering twice as many observation points.
ADVANCES IN WATER RESOURCES
(2021)
Article
Engineering, Civil
Xintong Wang, Xiang-Zhao Kong, Linwei Hu, Zhenhao Xu
Summary: This study explores the potential of hydraulic tomography (HT) in characterizing the distribution and connectivity of conduits in a two-dimensional sandbox and its corresponding synthetic aquifer. Two inversion techniques, geostatistics-based inversion and travel time-based inversion, were implemented and compared. The results show that both algorithms were able to identify the embedded karst conduits and yield similar hydraulic diffusivity distribution.
JOURNAL OF HYDROLOGY
(2023)
Article
Engineering, Civil
Zhanfeng Zhao, Walter A. Illman
Summary: The study utilized an inverse modeling approach to estimate K values directly from HPT survey data. It focused on developing site-specific formulae, demonstrating the joint implementation of HPT and HT techniques for high-resolution characterization of subsurface heterogeneity.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Yue Zhao, Quan Guo, Chunhui Lu, Jian Luo
Summary: A upscaling-based inverse approach, UPCIA, is developed to reduce the computational cost of gradient-based inverse methods for high-resolution groundwater flow inverse problems. It achieves dimensionality reduction by evaluating the Jacobian through upscaled effective models on a coarse-resolution grid. Various numerical experiments demonstrate its effectiveness and efficiency in estimating parameter fields and reducing computation time significantly.
WATER RESOURCES RESEARCH
(2022)
Article
Engineering, Civil
Quan Guo, Yue Zhao, Chunhui Lu, Jian Luo
Summary: A hydraulic tomography - physics informed neural network (HT-PINN) is developed to efficiently and accurately invert large-scale spatially distributed transmissivity fields. The method involves jointly training all neural network models to minimize the total loss function, resulting in inversion map accuracy exceeding 95%. It also exhibits great scalability and structure robustness in inverting fields with different resolutions.
JOURNAL OF HYDROLOGY
(2023)
Review
Engineering, Chemical
Wenyang Shi, Guangzhi Yin, Mi Wang, Lei Tao, Mengjun Wu, Zhihao Yang, Jiajia Bai, Zhengxiao Xu, Qingjie Zhu
Summary: This article introduces the application of Electrical Resistance Tomography (ERT) in monitoring the dynamics of oil and gas reservoirs. By measuring the distribution of resistance, ERT can provide information on subsurface media. The article reviews the progress of ERT in dynamic monitoring of reservoirs and its technological optimization, emphasizing the importance of ERT in improving production efficiency and reducing risk.
Article
Engineering, Civil
A. Jardani, T. M. Vu, P. Fischer
Summary: In this manuscript, the capabilities of a deep learning algorithm called CNN-HT to characterize hydraulic properties of aquifers were discussed. The method relies on an adaptation of the SegNet network for predicting hydraulic tomography, and has shown effectiveness in reconstructing main heterogeneities in hydraulic properties.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
Mohammed Aliouache, Xiaoguang Wang, Pierre Fischer, Gerard Massonnat, Herve Jourde
Summary: The combination of tomographic pumping and flowmeter tests in an inverse approach allows for efficient characterization of three-dimensional aquifer properties, enhancing the realism of depositional features. This method offers a low-cost and rapid assessment of hydraulic properties in layered porous rocks, with good agreement between inverted hydraulic conductivity field and permeability measurements.
JOURNAL OF HYDROLOGY
(2021)
Article
Water Resources
Hojat Ghorbanidehno, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Eric F. Darve, Peter K. Kitanidis
Summary: Riverine bathymetry is important for shipping and flood management, and indirect measurements with sensor technology can be used to estimate river bed topography. Physics-based techniques are computationally expensive, while deep learning offers a data-driven approach with potential for efficient training using limited data. The proposed method combines DNN with PCA to image river bed topography using flow velocity observations, showing satisfactory performance in bathymetry estimation with low computational cost and small number of training samples.
ADVANCES IN WATER RESOURCES
(2021)
Article
Geosciences, Multidisciplinary
Michael N. Fienen, Nicholas T. Corson-Dosch, Jeremy T. White, Andrew T. Leaf, Randall J. Hunt
Summary: This paper introduces a risk-based approach to environmental water management, which evaluates posterior uncertainty by integrating prior information, building decision models, and conducting data assimilation to address errors in model construction and data assimilation. By generating a scripted workflow, groundwater model construction, data assimilation, and well water source issues in a risk framework can be efficiently addressed.
Article
Mathematics, Interdisciplinary Applications
Peter K. Kitanidis
Summary: This paper discusses the application of covariance and Fisher information matrix in inverse problems, as well as a reexamination within the Bayesian framework, proposing a lower bound for the covariance of the posterior probability density function.
GEM-INTERNATIONAL JOURNAL ON GEOMATHEMATICS
(2021)
Article
Engineering, Civil
Xueyuan Kang, Amalia Kokkinaki, Christopher Power, Peter K. Kitanidis, Xiaoqing Shi, Limin Duan, Tingxi Liu, Jichun Wu
Summary: The proposed geostatistical data assimilation method, using deep learning techniques, significantly improves the monitoring of DNAPL remediation. Experimental results show a reduction of 51% in the estimation error of DNAPL mass remediation compared to the standard EnKF method, indicating better real-time monitoring of DNAPL remediation.
JOURNAL OF HYDROLOGY
(2021)
Article
Multidisciplinary Sciences
Yi-Lin Tsai, Chetanya Rastogi, Peter K. Kitanidis, Christopher B. Field
Summary: The study discusses the importance of integrating social distancing with emergency evacuation operations and found that deep reinforcement learning can provide more efficient routing compared to other solutions. However, the time saved by deep reinforcement learning in evacuation does not compensate for the extra time required for social distancing as the emergency vehicle capacity approaches the number of people per household.
SCIENTIFIC REPORTS
(2021)
Article
Computer Science, Interdisciplinary Applications
Jeremy T. White, Matthew J. Knowling, Michael N. Fienen, Adam Siade, Otis Rea, Guillermo Martinez
Summary: An open-source tool has been developed for constrained single-and multi-objective optimization under uncertainty (CMOU) analyses. It uses PEST interface protocols to communicate with the underlying forward simulation and contains a built-in parallel run manager for distributed computing resources. The tool implements popular evolutionary algorithms and various approaches to represent uncertainty in model-derived constraint/objective values. This tool addresses the barrier to adopting advanced CMOU analyses for decision-support problems in environmental modeling.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Computer Science, Interdisciplinary Applications
Ayman H. Alzraiee, Jeremy T. White, Matthew J. Knowling, Randall J. Hunt, Michael N. Fienen
Summary: This study introduces an open-source software tool, PESTPP-DA, that enables efficient and scalable model parameter estimation using various data assimilation methods, and supports interface protocols with any model. The broad applications of PESTPP-DA are demonstrated through two synthetic case studies.
ENVIRONMENTAL MODELLING & SOFTWARE
(2022)
Article
Environmental Sciences
Lijing Wang, Peter K. Kitanidis, Jef Caers
Summary: Bayesian inversion is commonly used to quantify uncertainty of hydrological variables. This paper proposes a hierarchical Bayesian framework to quantify uncertainty of both global and spatial variables. The authors present a machine learning-based inversion method and a local dimension reduction method to efficiently estimate posterior probabilities and update spatial fields. Using three case studies, they demonstrate the importance of quantifying uncertainty of global variables for predictions and the acceleration effect of the local PCA approach.
WATER RESOURCES RESEARCH
(2022)
Article
Water Resources
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
Summary: This article presents a reduced-order model (ROM) based approach that utilizes a variational autoencoder (VAE) to compress bathymetry and flow velocity information, allowing for fast solving of bathymetry inverse problems. By constructing ROMs on a nonlinear manifold and employing a Hierarchical Bayesian setting, variational inference and efficient uncertainty quantification can be achieved using a small number of ROM runs.
ADVANCES IN WATER RESOURCES
(2022)
Editorial Material
Geosciences, Multidisciplinary
Andrew T. Leaf, Michael N. Fienen
Book Review
Geosciences, Multidisciplinary
Michael Fienen
Article
Environmental Sciences
Xueyuan Kang, Amalia Kokkinaki, Xiaoqing Shi, Hongkyu Yoon, Jonghyun Lee, Peter K. Kitanidis, Jichun Wu
Summary: This study presents a framework that combines a deep-learning-based inversion method with a process-based upscaled model to estimate source zone architecture (SZA) metrics and mass discharge from sparse data. By improving the estimation method, the upscaled model accurately reproduces the concentrations and uncertainties of multistage effluents, providing valuable input for decision making in remediation applications.
WATER RESOURCES RESEARCH
(2022)
Article
Environmental Sciences
Simon Meunier, Peter K. Kitanidis, Amaury Cordier, Alan M. MacDonald
Summary: This study develops a numerical model to simulate the abstraction capacities of photovoltaic water pumping systems across Africa using openly available data. The model includes realistic geological constraints on pumping depth and sub-hourly irradiance time series. The simulation results show that for much of Africa, groundwater pumping using photovoltaic energy is limited by aquifer conditions rather than irradiance. These findings can help identify regions with high potential for photovoltaic pumping and guide large-scale investments.
COMMUNICATIONS EARTH & ENVIRONMENT
(2023)
Editorial Material
Geosciences, Multidisciplinary
Jonathan P. Traylor, Randall J. Hunt, Jeremy White, Michael N. Fienen
Article
Geosciences, Multidisciplinary
Anthony M. Castronova, Ayman Nassar, Wouter Knoben, Michael N. Fienen, Louise Arnal, Martyn Clark