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
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
Geosciences, Multidisciplinary
Shizuka Takai, Taro Shimada, Seiji Takeda, Katsuaki Koike
Summary: In order to accurately estimate the remediation of accidental groundwater contamination, this study develops a geostatistical method to jointly clarify the contaminant plume and transmissivity distributions using both head and contaminant concentration data. The proposed method was demonstrated to be applicable and effective through two numerical experiments in a two-dimensional heterogeneous confined aquifer. The use of contaminant concentration data was found to be key in accurately estimating the transmissivity, and the uncertainty of the contaminant plume evolution was successfully evaluated.
MATHEMATICAL GEOSCIENCES
(2023)
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
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 Liu, Rui Hu, Linwei Hu, Yixuan Xing, Pengxiang Qiu, Huichen Yang, Steffen Fischer, Thomas Ptak
Summary: The study modified the inversion framework of thermal tracer tomography to improve the characterization of hydraulic properties in fractured aquifers. Results show that the modified method can efficiently identify fractures connectivity and conduct hydraulic property characterization accurately under different conditions.
JOURNAL OF HYDROLOGY
(2022)
Article
Engineering, Civil
M. T. Vu, A. Jardani
Summary: This paper introduces a new concept of using convolutional neural networks to map hydraulic transmissivity. The relationship between concentration data and transmissivity field is established through two networks, which are trained and reconstructed to obtain accurate transmissivity fields.
JOURNAL OF HYDROLOGY
(2022)
Article
Environmental Sciences
Zhenjiao Jiang, Lisa Maria Ringel, Peter Bayer, Tianfu Xu
Summary: In this study, a joint inversion procedure is developed to infer fracture number, geometry, and aperture based on Bayesian principles, utilizing microseismic events and thermal breakthrough data. The methodology is applied to two synthetic test cases and is confirmed to approximate the fracture geometry and aperture well. This method provides a new way to characterize fracture networks in the subsurface without restrictions on predetermined fracture sizes or the number of fractures.
WATER RESOURCES RESEARCH
(2023)
Article
Geosciences, Multidisciplinary
Yu-Li Wang, Tian-Chyi Jim Yeh, Fei Liu, Jet-Chau Wen, Wenke Wang, Yonghong Hao
Summary: This paper utilizes triggered lightning as a point source for electromagnetic tomographic survey to image 3-D subsurface electrical properties in basins. It introduces a new temporal moment approach that overcomes the challenges in modeling 3-D Maxwell's equations with heterogeneous parameter fields. The results show that this approach can detect signals within a radius of 20-70 km and is suitable for mapping subsurface electric conductivity heterogeneity.
GEOPHYSICAL RESEARCH LETTERS
(2022)
Article
Engineering, Civil
Zheng Han, Xueyuan Kang, Jichun Wu, Xiaoqing Shi
Summary: Accurate characterization of hydraulic properties is crucial for understanding groundwater flow and contaminant transport. In this study, a deep learning-based inversion framework was used to jointly invert hydraulic head and self-potential (SP) data to characterize non-Gaussian hydraulic conductivity (K) fields. The results demonstrate that integrating hydraulic head and SP data improves the accuracy and reduces the uncertainty of non-Gaussian K field reconstruction.
JOURNAL OF HYDROLOGY
(2022)
Article
Water Resources
Lukas Roemhild, Gianluca Fiandaca, Linwei Hu, Laura Meyer, Peter Bayer
Summary: This study presents a new inversion procedure that allows for the direct computation of hydraulic conductivity (K) in an aquifer using induced polarization (IP) data. The novel approach was successfully implemented and showed a similar quality compared to hydraulic tomography. The results highlight the accuracy of the inversion and the significance of the proposed calibration strategies.
ADVANCES IN WATER RESOURCES
(2022)
Article
Environmental Sciences
Lisa Maria Ringel, Mohammadreza Jalali, Peter Bayer
Summary: This study presents an approach for stochastic characterization of geometric and hydraulic parameters of a 3D discrete fracture network (DFN) based on hydraulic tomography data and estimation of their uncertainty through Bayesian framework and Markov chain Monte Carlo (MCMC) methods. The method is effective in identifying variable fracture locations and orientations, especially for preferential flow paths, and discriminating fractures based on reliability. Additionally, it demonstrates the calibration of hydraulic apertures with fracture geometries and the necessity of complementing hydraulic measurements with additional information for successful inversion.
WATER RESOURCES RESEARCH
(2021)
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
Geosciences, Multidisciplinary
Rasmus Bodker Madsen, Hyojin Kim, Anders Juhl Kallesoe, Peter B. E. Sandersen, Troels Norvin Vilhelmsen, Thomas Mejer Hansen, Anders Vest Christiansen, Ingelise Moller, Birgitte Hansen
Summary: Nitrate contamination in subsurface aquifers is a persistent environmental challenge, mainly caused by nitrogen leaching from intensive fertilization in agriculture. A novel approach was proposed to model both geology and redox architectures simultaneously in high resolution 3D, aiming to better understand nitrogen transport in the subsurface and potentially lead to more targeted regulation of agriculture.
HYDROLOGY AND EARTH SYSTEM SCIENCES
(2021)
Article
Biochemical Research Methods
Jonas Meisner, Siyang Liu, Mingxi Huang, Anders Albrechtsen
Summary: The article introduces a method EMU for inferring population structure in the presence of massive missing data, which is both fast and accurate. Testing on the Chinese Millionome Project dataset demonstrates the effectiveness of EMU in capturing population structure.
Article
Engineering, Environmental
Mojtaba Forghani, Yizhou Qian, Jonghyun Lee, Matthew W. Farthing, Tyler Hesser, Peter K. Kitanidis, Eric F. Darve
Summary: This study proposes a two-stage process utilizing principal component geostatistical approach to estimate bathymetry probability density function and multiple machine learning algorithms to solve shallow water equations (SWEs) efficiently. The first stage predicts flow velocities without direct bathymetry measurement, while the second stage incorporates additional bathymetry information for improved accuracy and generalization. Fast solvers are capable of accurately predicting flow velocities with variable bathymetry and BCs at a significantly lower computational cost compared to traditional methods.
STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
(2021)
Article
Environmental Sciences
Xueyuan Kang, Amalia Kokkinaki, Peter K. Kitanidis, Xiaoqing Shi, Jonghyun Lee, Shaoxing Mo, Jichun Wu
Summary: Characterizing the architecture of dense nonaqueous phase liquid (DNAPL) source zones is crucial for designing efficient remediation strategies, but traditional drilling investigations provide limited information and affect the accuracy of geostatistical methods. By parameterizing the DNAPL saturation field using a physics-based approach, improved prior descriptions and better resolution can be achieved in characterizing the source zones. Additionally, incorporating hydrogeological and geophysical datasets in the inversion framework can further enhance the performance of the method.
WATER RESOURCES RESEARCH
(2021)
Article
Geosciences, Multidisciplinary
Brytne K. Okuhata, Aly El-Kadi, Henrietta Dulai, Jonghyun Lee, Christopher A. Wada, Leah L. Bremer, Kimberly M. Burnett, Jade M. S. Delevaux, Christopher K. Shuler
Summary: The study highlights the importance of fresh groundwater in supporting coastal ecosystems and the threats posed by contamination. A density-dependent groundwater model was found to be more effective in managing this complex hydrogeologic system. Environmental changes such as sea-level rise have significant impacts on groundwater levels and quality, emphasizing the importance of considering model boundaries and parameters.
HYDROGEOLOGY JOURNAL
(2022)
Article
Geochemistry & Geophysics
Dawoon Lee, Jonghyun Lee, Changsoo Shin, Sungryul Shin, Wookeen Chung
Summary: The study proposes seismic elastic full-waveform inversion (EFWI) using both multiparametric approximate Hessian and the discrete cosine transform (DCT) to reduce computational burden and increase efficiency. Numerical experiments demonstrated that contamination due to crosstalk artifacts can be suppressed using multiparametric approximate Hessian in EFWI, even with a small number of DCT coefficients.
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
(2022)
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
Meteorology & Atmospheric Sciences
Matthew P. Lucas, Ryan J. Longman, Thomas W. Giambelluca, Abby G. Frazier, Jared McLean, Sean B. Cleveland, Yu-Fen Huang, Jonghyun Lee
Summary: This study applies an optimized geostatistical kriging approach to obtain high-resolution gridded monthly rainfall time series for Hawaii. The results are validated using cross-validation and show good agreement with observations, although there may be underestimation of high rainfall events due to the smoothing effect of kriging. The study highlights the importance of considering additional sources of error assessment and modifying parameterizations for realistic gridded rainfall surfaces.
JOURNAL OF HYDROMETEOROLOGY
(2022)
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
Engineering, Civil
Brytne K. Okuhata, Donald M. Thomas, Henrietta Dulai, Brian N. Popp, Jonghyun Lee, Aly El-Kadi
Summary: The geologically complex western aquifers of Hawai'i Island serve as the primary reservoir of fresh potable water for residents, yet the area's hydrogeologic characteristics are still not well understood. This study implemented a multi-tracer approach to estimate the apparent ages of groundwater in the West Hawai'i aquifers. The results showed the presence of both young and old groundwater, with important implications for groundwater management.
JOURNAL OF HYDROLOGY
(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)
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
Tristan McKenzie, Henrietta Dulai, Jonghyun Lee, Natasha T. Dimova, Isaac R. Santos, Bo Zhang, William Burnett
Summary: This study utilizes deep learning to predict radon concentrations in coastal waters impacted by submarine groundwater discharge. By training two deep learning models, it is possible to predict observed radon concentrations using readily available input parameters, offering an opportunity to obtain radon data in regions with limited data availability.
WATER RESOURCES RESEARCH
(2023)
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)