Integrating deep learning-based data assimilation and hydrogeophysical data for improved monitoring of DNAPL source zones during remediation
暂无分享,去创建一个
P. Kitanidis | Jichun Wu | Xiaoqing Shi | L. Duan | Christopher Power | A. Kokkinaki | X. Kang | Ting-xi Liu
[1] P. Kitanidis,et al. Hydrogeophysical Characterization of Nonstationary DNAPL Source Zones by Integrating a Convolutional Variational Autoencoder and Ensemble Smoother , 2021, Water Resources Research.
[2] Eric Darve,et al. Recent developments in fast and scalable inverse modeling and data assimilation methods in hydrology , 2020 .
[3] P. Kitanidis,et al. Improved Characterization of DNAPL Source Zones via Sequential Hydrogeophysical Inversion of Hydraulic‐Head, Self‐Potential and Partitioning Tracer Data , 2020, Water Resources Research.
[4] Alexander Y. Sun,et al. Inversion of Time‐Lapse Seismic Reservoir Monitoring Data Using CycleGAN: A Deep Learning‐Based Approach for Estimating Dynamic Reservoir Property Changes , 2020, Journal of Geophysical Research: Solid Earth.
[5] M. Clara De Paolis Kaluza,et al. Subsurface Source Zone Characterization and Uncertainty Quantification Using Discriminative Random Fields , 2020, Water Resources Research.
[6] A. Revil,et al. Multiscale induced polarization tomography in hydrogeophysics: A new approach , 2019, Advances in Water Resources.
[7] Marco Aurélio Cavalcanti Pacheco,et al. Towards a Robust Parameterization for Conditioning Facies Models Using Deep Variational Autoencoders and Ensemble Smoother , 2018, Comput. Geosci..
[8] Henry Lau,et al. Design of optimal groundwater monitoring well network using stochastic modeling and reduced‐rank spatial prediction , 2017 .
[9] Eric Darve,et al. Smoothing‐based compressed state Kalman filter for joint state‐parameter estimation: Applications in reservoir characterization and CO2 storage monitoring , 2017 .
[10] M. Cardiff,et al. Oscillatory hydraulic testing as a strategy for NAPL source zone monitoring: Laboratory experiments. , 2017, Journal of contaminant hydrology.
[11] Peter K. Kitanidis,et al. Scalable subsurface inverse modeling of huge data sets with an application to tracer concentration breakthrough data from magnetic resonance imaging , 2016 .
[12] W. Nowak,et al. Identification of contaminant source architectures—A statistical inversion that emulates multiphase physics in a computationally practicable manner , 2016 .
[13] Paolo Salandin,et al. Coupled and uncoupled hydrogeophysical inversions using ensemble Kalman filter assimilation of ERT‐monitored tracer test data , 2015 .
[14] Wolfgang Nowak,et al. Predicting DNAPL mass discharge and contaminated site longevity probabilities: Conceptual model and high‐resolution stochastic simulation , 2015 .
[15] J. Gerhard,et al. Evaluating four-dimensional time-lapse electrical resistivity tomography for monitoring DNAPL source zone remediation. , 2014, Journal of contaminant hydrology.
[16] Aaron C. Courville,et al. Generative adversarial networks , 2014, Commun. ACM.
[17] Eric Darve,et al. A Kalman filter powered by H2 ‐matrices for quasi‐continuous data assimilation problems , 2014, ArXiv.
[18] Wolfgang Nowak,et al. A method for implementing Dirichlet and third‐type boundary conditions in PTRW simulations , 2014 .
[19] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[20] Denis M. O'Carroll,et al. Coupled simulation of DNAPL infiltration and dissolution in three‐dimensional heterogeneous domains: Process model validation , 2013 .
[21] P. Kitanidis,et al. Aquifer heterogeneity characterization with oscillatory pumping: Sensitivity analysis and imaging potential , 2013 .
[22] J. Gerhard,et al. A new coupled model for simulating the mapping of dense nonaqueous phase liquids using electrical resistivity tomography , 2013 .
[23] Albert C. Reynolds,et al. Ensemble smoother with multiple data assimilation , 2013, Comput. Geosci..
[24] E. Miller,et al. A geometric approach to joint inversion with applications to contaminant source zone characterization , 2013, 1303.5109.
[25] Charles J Newell,et al. Groundwater Remediation: The Next 30 Years , 2012, Ground water.
[26] H. Franssen,et al. Parameter estimation by ensemble Kalman filters with transformed data: Approach and application to hydraulic tomography , 2012 .
[27] Rosemary Knight,et al. Application of an extended Kalman filter approach to inversion of time‐lapse electrical resistivity imaging data for monitoring recharge , 2011 .
[28] Olaf A. Cirpka,et al. Fully coupled hydrogeophysical inversion of synthetic salt tracer experiments , 2010 .
[29] Linda M Abriola,et al. Predicting DNAPL mass discharge from pool-dominated source zones. , 2010, Journal of contaminant hydrology.
[30] R. Glass,et al. Interphase mass transfer in variable aperture fractures: Controlling parameters and proposed constitutive relationships , 2009 .
[31] Dean S. Oliver,et al. An Iterative Ensemble Kalman Filter for Multiphase Fluid Flow Data Assimilation , 2007 .
[32] Tian-Chyi J. Yeh,et al. Hydraulic/partitioning tracer tomography for characterization of dense nonaqueous phase liquid source zones , 2007 .
[33] P. Goovaerts,et al. A geostatistical approach for quantification of contaminant mass discharge uncertainty using multilevel sampler measurements , 2007 .
[34] J. Gerhard,et al. Field scale impacts of spatially correlated relative permeability in heterogeneous multiphase systems , 2007 .
[35] E. Sudicky,et al. Transition Probability/Markov Chain Analyses of DNAPL Source Zones and Plumes , 2006, Ground water.
[36] Kurt D. Pennell,et al. Estimating mass discharge from dense nonaqueous phase liquid source zones using upscaled mass transfer coefficients: An evaluation using multiphase numerical simulations , 2006 .
[37] B. Minsker,et al. Spatial Interpolation Methods for Nonstationary Plume Data , 2004, Ground water.
[38] Michael C Kavanaugh,et al. The DNAPL Remediation Challenge: Is There a Case for Source Depletion? , 2003 .
[39] Jason I. Gerhard,et al. Relative permeability characteristics necessary for simulating DNAPL infiltration, redistribution, and immobilization in saturated porous media , 2003 .
[40] W. Graham,et al. Estimation of spatially variable residual nonaqueous phase liquid saturations in nonuniform flow fields using partitioning tracer data , 2000 .
[41] P.A. Karjalainen,et al. A Kalman filter approach to track fast impedance changes in electrical impedance tomography , 1998, IEEE Transactions on Biomedical Engineering.
[42] Brian Berkowitz,et al. A generalized growth model for simulating initial migration of dense non‐aqueous phase liquids , 1998 .
[43] P. Kitanidis. Introduction to Geostatistics: Applications in Hydrogeology , 1997 .
[44] G. Evensen. Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .
[45] C. R. Dietrich,et al. A fast and exact method for multidimensional gaussian stochastic simulations , 1993 .
[46] Jack C. Parker,et al. A model for hysteretic constitutive relations governing multiphase flow: 3. Refinements and numerical simulations , 1989 .
[47] J. Bear. Dynamics of Fluids in Porous Media , 1975 .
[48] P. A. Domenico,et al. Water from low‐permeability sediments and land subsidence , 1965 .
[49] G. E. Archie. The electrical resistivity log as an aid in determining some reservoir characteristics , 1942 .
[50] Geoffrey E. Hinton,et al. Deep Learning , 2015 .
[51] J. Gerhard,et al. Improved time-lapse electrical resistivity tomography monitoring of dense non-aqueous phase liquids with surface-to-horizontal borehole arrays , 2015 .
[52] L. Abriola,et al. Source Remediation Challenges , 2012 .
[53] D. Oliver,et al. Ensemble Randomized Maximum Likelihood Method as an Iterative Ensemble Smoother , 2011, Mathematical Geosciences.
[54] John A. Cherry,et al. Dense Chlorinated Solvents and other DNAPLs in Groundwater , 1996 .