Velimir V Vesselinov ("monty") | Los Alamos National Laboratory (original) (raw)

Papers by Velimir V Vesselinov ("monty")

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 15, 2020

Groundwater pumping of water-supply wells frequently exhibits substantial temporal and spatial va... more Groundwater pumping of water-supply wells frequently exhibits substantial temporal and spatial variability. Wells are typically operated periodically (on daily and seasonal temporal scales), and the total production is controlled by the water-supply demand,. The pumping causes temporal and spatial variability in the water levels around the pumping wells. During the wellfield operation, water-level data are collected from the production and

arXiv: Geophysics, 2011

It has been previously observed that during a pumping test in heterogeneous media, drawdown data ... more It has been previously observed that during a pumping test in heterogeneous media, drawdown data from dierent time periods collected at a single location produce dierent estimates of aquifer properties and that Theis type-curve inferences are more variable than late-time Cooper-Jacob inferences. In order to obtain estimates of aquifer properties from highly transient drawdown data using the Theis solution, it is necessary to account for this behavior. We present an approach that utilizes an exponential functional form to represent Theis parameter behavior resulting from the spatial propagation of a cone of depression. This approach allows the use of transient data consisting of early-time drawdown data to obtain late-time convergent Theis parameters consistent with Cooper-Jacob method inferences. We demonstrate the approach on a multi-year dataset consisting of multi-well transient water-level observations due to transient multi-well water-supply pumping. Based on previous research,...

Applied Mathematical Modelling, 2018

The identification of sources of advection-diffusion transport is based usually on solving comple... more The identification of sources of advection-diffusion transport is based usually on solving complex ill-posed inverse models against the available statevariable data records. However, if there are several sources with different locations and strengths, the data records represent mixtures rather than the separate influences of the original sources. Importantly, the number of these original release sources is typically unknown, which hinders reliability of the classical inverse-model analyses. To address this challenge, we present here a novel hybrid method for identification of the unknown number of release sources. Our hybrid method, called HNMF, couples unsupervised learning based on Non-negative Matrix Factorization (NMF) and inverse-analysis Green's functions method. HNMF synergistically performs decomposition of the recorded mixtures, finds the number of the unknown sources and uses the Green's function of advection-diffusion equation to identify their characteristics. In the paper, we introduce the method and demonstrate that it is capable of identifying the advection velocity and dispersivity of the medium as well as the unknown number, locations, and properties of various sets of synthetic release sources with different space and time dependencies, based only on the recorded data. HNMF can be applied directly to any problem controlled by a partial-differential parabolic equation where mixtures of an unknown number of sources are measured at multiple locations.

PLOS ONE, 2018

D-Wave quantum annealers represent a novel computational architecture and have attracted signific... more D-Wave quantum annealers represent a novel computational architecture and have attracted significant interest. Much of this interest has focused on the quantum behavior of D-Wave machines, and there have been few practical algorithms that use the D-Wave. Machine learning has been identified as an area where quantum annealing may be useful. Here, we show that the D-Wave 2X can be effectively used as part of an unsupervised machine learning method. This method takes a matrix as input and produces two low-rank matrices as output-one containing latent features in the data and another matrix describing how the features can be combined to approximately reproduce the input matrix. Despite the limited number of bits in the D-Wave hardware, this method is capable of handling a large input matrix. The D-Wave only limits the rank of the two output matrices. We apply this method to learn the features from a set of facial images and compare the performance of the D-Wave to two classical tools. This method is able to learn facial features and accurately reproduce the set of facial images. The performance of the D-Wave shows some promise, but has some limitations. It outperforms the two classical codes in a benchmark when only a short amount of computational time is allowed (200-20,000 microseconds), but these results suggest heuristics that would likely outperform the D-Wave in this benchmark.

Water Resources Research, 2015

Sensitivity analyses are an important component of any modeling exercise. We have developed an an... more Sensitivity analyses are an important component of any modeling exercise. We have developed an analytical methodology based on the adjoint method to compute sensitivities of a state variable (hydraulic head) to model parameters (hydraulic conductivity and storage coefficient) for transient groundwater flow in a confined and randomly heterogeneous aquifer under ambient and pumping conditions. For a special case of two-dimensional rectangular domains, these sensitivities are represented in terms of the problem configuration (the domain size, boundary configuration, medium properties, pumping schedules and rates, and observation locations and times), and there is no need to actually solve the adjoint equations. As an example, we present analyses of the obtained solution for typical groundwater flow conditions. Analytical solutions allow us to calculate sensitivities efficiently, which can be useful for model-based analyses such as parameter estimation, data-worth evaluation, and optimal experimental design related to sampling frequency and locations of observation wells. The analytical approach is not limited to groundwater applications but can be extended to any other mathematical problem with similar governing equations and under similar conceptual conditions.

Model simulations are widely used in environmental management decision processes. However, there ... more Model simulations are widely used in environmental management decision processes. However, there are various sources of uncertainty that commonly impact the model results. Consequently, it is crucial to account for all the possible model uncertainties that impact the model results so that they are adequately considered in the management decision process. Here we discuss an uncertainty analysis of model simulations

Efficiency evaluation of a groundwater monitoring network using a risk-based decision-support pro... more Efficiency evaluation of a groundwater monitoring network using a risk-based decision-support process Velimir V Vesselinov, Kay Birdsell, Paul David, Chris Echohawk A series of contaminant sources have the potential to impact the quality of regional groundwater resources beneath the Los Alamos National Laboratory (LANL). Currently, 21 areas have been identified where contaminants could possibly reach the regional aquifer at the

There are various methods that can be applied to identify the potential spatial locations of cont... more There are various methods that can be applied to identify the potential spatial locations of contaminant sources in regional aquifers. We propose and apply an inverse methodology that takes into account directly the various uncertainties associated with the available hydrogeological information (conceptual uncertainties, observation errors, parameter uncertainties). The technique utilizes a series of forward simulations of probable contaminant transport that

 Model-based Decision Support  Deterministic, Probabilistic vs Non-Probabilistic Decision Metho... more  Model-based Decision Support  Deterministic, Probabilistic vs Non-Probabilistic Decision Methods  Information Gap (info-gap) Decision Theory  Decision Support for Chromium contamination site @ LANL o Site conceptual model o Model-based decision analyses o Monitoring network design o Additional activities related to contaminant remediation

The Los Alamos National Laboratory continuously monitors groundwater levels and surface-water dis... more The Los Alamos National Laboratory continuously monitors groundwater levels and surface-water discharge at over 150 locations on the Pajarito Plateau. The resulting data sets were analyzed to help identify locations where surface water and shallow alluvial groundwater (generally <30 ft) recharge deeper perched- intermediate groundwater (approximately 200 to 700 ft bgs) zones. Runoff from snowmelt and summer rainstorms recharges the

Water Resources Research, 2014

The identification of the physical sources causing spatial and temporal fluctuations of aquifer w... more The identification of the physical sources causing spatial and temporal fluctuations of aquifer water levels is a challenging, yet a very important hydrogeological task. The fluctuations can be caused by variations in natural and anthropogenic sources such as pumping, recharge, barometric pressures, etc. The source identification can be crucial for conceptualization of the hydrogeological conditions and characterization of aquifer properties. We propose a new computational framework for model-free inverse analysis of pressure transients based on Nonnegative Matrix Factorization (NMF) method for Blind Source Separation (BSS) coupled with k-means clustering algorithm, which we call NMFk. NMFk is capable of identifying a set of unique sources from a set of experimentally measured mixed signals, without any information about the sources, their transients, and the physical mechanisms and properties controlling the signal propagation through the subsurface flow medium. Our analysis only requires information about pressure transients at a number of observation points, m, where m ! r, and r is the number of unknown unique sources causing the observed fluctuations. We apply this new analysis on a data set from the Los Alamos National Laboratory site. We demonstrate that the sources identified by NMFk have real physical origins: barometric pressure and water-supply pumping effects. We also estimate the barometric pressure efficiency of the monitoring wells. The possible applications of the NMFk algorithm are not limited to hydrogeology problems; NMFk can be applied to any problem where temporal system behavior is observed at multiple locations and an unknown number of physical sources are causing these fluctuations.

Hydrogeologic parameters representing aquifer transmissivity and storativity appear to be spatial... more Hydrogeologic parameters representing aquifer transmissivity and storativity appear to be spatially dependent due to aquifer heterogeneity. It has been demonstrated that during a pumping test, drawdown data from different time periods produces different estimates of aquifer properties (e.g. early-time Theis type-curve vs. late-time Copper-Jacob inference methods). This suggests that as the cone of depression propagates through the aquifer, spatially-dependent aquifer properties control the drawdown behavior. For example, transmissivity inferences from early-time drawdown data produce time-dependent results, while late-time data inferences are typically found to converge towards a single value. In order to infer effective properties, it may be critical to account for the temporal (spatial) dependency of hydrogeologic parameters during the pumping test. We present an approach that accounts for these effects by considering alternative functional representations of the potential tempora...

It has been previously observed that during a pumping test in heterogeneous media, drawdown data ... more It has been previously observed that during a pumping test in heterogeneous media, drawdown data from different time periods collected at a single location produce different estimates of aquifer properties and that Theis type-curve inferences are more variable than late-time Cooper-Jacob inferences. In order to obtain estimates of aquifer properties from highly transient drawdown data using the Theis solution, it is necessary to account for this behavior. We present an approach that utilizes an exponential functional form to represent Theis parameter behavior resulting from the spatial propagation of a cone of depression. This approach allows the use of transient data consisting of early-time drawdown data to obtain late-time convergent Theis parameters consistent with Cooper-Jacob method inferences. We demonstrate the approach on a multi-year dataset consisting of multi-well transient water-level observations due to transient multi-well water-supply pumping. Based on previous resea...

Vadose Zone Journal, 2008

OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information), Sep 15, 2020

arXiv: Geophysics, 2011

Applied Mathematical Modelling, 2018

PLOS ONE, 2018

Water Resources Research, 2015

Water Resources Research, 2014

It has been previously observed that during a pumping test in heterogeneous media, drawdown data ... more It has been previously observed that during a pumping test in heterogeneous media, drawdown data from different time periods collected at a single location produce different estimates of aquifer properties and that Theis type-curve inferences are more variable than late-time Cooper-Jacob inferences. In order to obtain estimates of aquifer properties from highly transient drawdown data using the Theis solution, it is necessary to account for this behavior. We present an approach that utilizes an exponential functional form to represent Theis parameter behavior resulting from the spatial propagation of a cone of depression. This approach allows the use of transient data consisting of early-time drawdown data to obtain late-time convergent Theis parameters consistent with Cooper-Jacob method inferences. We demonstrate the approach on a multi-year dataset consisting of multi-well transient water-level observations due to transient multi-well water-supply pumping. Based on previous resea...

Vadose Zone Journal, 2008

In "Enhanced heap leaching - Part I: Insights" (Orr, 2002), different flow and transport phenomen... more In "Enhanced heap leaching - Part I: Insights" (Orr, 2002), different flow and transport phenomena that could significantly reduce leaching recovery were investigated. The combination of such understanding with advanced flow and transport modeling establishes the link between cause and effect, thereby, directing operators to optimal design and construction of new heaps. Modeling of flow and transport in heaps could also point to a unique change in application method or rate that would maximize leaching enhancement of an existing heap under existing situation and leaching history. The use of such a model is cost effective in that it can simulate multiple scenarios of alternative leaching enhancement methods. By simulating a large number of irrigation scenarios, such a model can point to the optimal and/or the most promising alternatives for a particular heap. The authors' simulations concentrate on the effects of changes in application rates and heap design on changes in flow paths and enhanced leaching recovery. Based on such simulations, an intelligent control agent, such as Multiple Resolution Decision-Support System (MRDS) (Meystel and Albus, 2002), could build a new representation of the complex heap-leaching system. And, by learning from historical data and current information, it could provide optimal management of heap leaching and heap decommissioning.

A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse ana... more A new adaptive hybrid optimization strategy, entitled squads, is proposed for complex inverse analysis of computationally intensive physics-based models. Typically, models are calibrated and model parameters are estimated by minimization of the discrepancy between model simulations characterizing the system and existing observations requiring a substantial number of model evaluations. Squads is designed to be computationally efficient and robust in identification of the global optimum (i.e. maximum or minimum value of an objective function). It integrates global and local optimization using Adaptive Particle Swarm Optimization (APSO) and Levenberg-Marquardt (LM) optimization using adaptive rules based on runtime performance. The global strategy (APSO) optimizes the location of a set of solutions (particles) in the parameter space. The local strategy (LM) is applied only to a subset of the particles at different stages of the optimization based on the adaptive rules. After the LM adjustment of the subset of particle positions, the updated particles are returned to APSO. Therefore, squads is a global strategy that utilizes a local optimization speedup. The advantages of coupling APSO and LM in the manner implemented in squads is demonstrated by comparisons of squads performance against Levenberg-Marquardt (LM), Particle Swarm Optimization (PSO), Adaptive Particle Swarm Optimization (APSO; i.e. TRIBES), and an existing hybrid optimization strategy (hPSO). All the strategies are tested on 2D, 5D and 10D Rosenbrock and Griewank polynomial test functions and a synthetic hydrogeologic application to identify the source of a contaminant plume in an aquifer. Tests are performed using a series of runs with random initial guesses for the estimated parameters. The performance of the strategies are compared based on their robustness, defined as the percentage of runs that identify the global optimum, and their efficiency, quantified by a statistical representation of the number of function evaluations performed prior to identification of the global optimum. Squads is observed to have better performance than the other strategies for the test functions and the hydrogeologic application when both robustness and efficiency are taken into consideration. {\textcopyright} 2012.

\textcopyright} 2015. American Geophysical Union. All Rights Reserved.The problem of groundwater ... more \textcopyright} 2015. American Geophysical Union. All Rights Reserved.The problem of groundwater contamination in an aquifer is one with many uncertainties. Properly quantifying these uncertainties is essential in order to make reliable probabilistic-based predictions and decisions regarding remediation strategies. In this work, a measure-theoretic framework is employed to quantify uncertainties in a simplified groundwater contamination transport model. Given uncertain data from observation wells, the stochastic inverse problem is solved numerically to obtain a probability measure on the space of unknown model parameters characterizing groundwater flow and contaminant transport in an aquifer, as well as unknown model boundary or source terms such as the contaminant source release into the environment. This probability measure is used to make predictions of future contaminant concentrations and to analyze possible remediation techniques. The ability to identify regions of small but nonzero probability using this method is illustrated.

Single particle tracking is a tool that is being increasingly used to study diffusive or dispersi... more Single particle tracking is a tool that is being increasingly used to study diffusive or dispersive processes in many branches of natural science. Often the ability to collect these trajectories experimentally or produce them numerically outpaces the ability to understand them theoretically. On the other hand many stochastic models have been developed and continue to be developed capable of capturing complex diffusive behavior such as heavy tails, long-range correlations, nonstationarity, and combinations of these things. We describe a computational method for connecting particle trajectory data with stochastic models of diffusion. Several tests are performed to demonstrate the efficacy of the method, and the method is applied to polymer diffusion, RNA diffusion in E. coli, and RAFOS dispersion in the Gulf of Mexico. {\textcopyright} 2014 Springer Science+Business Media New York (Outside the U.S.).

Groundwater flow and transport often occur in a highly heterogeneous environment (potentially het... more Groundwater flow and transport often occur in a highly heterogeneous environment (potentially heterogeneous at multiple spatial scales) and is impacted by geochemical reactions, advection, diffusion, and other pore scale processes. All these factors can give rise to large-scale anomalous dispersive behavior that can make complex model representation and prediction of plume concentrations challenging due to difficulties unraveling all the complexities associated with the governing processes, flow medium, and their parameters. An alternative is to use upscaled stochastic models of anomalous dispersion, and this is the approach used here. Within a probabilistic framework, we derive a number of analytical solutions for several anomalous dispersion models. The anomalous dispersion models are allowed to be either non-Gaussian ($\alpha$-stable L{\'{e}}vy), correlated, or nonstationary from the Lagrangian perspective. A global sensitivity analysis is performed to gain a greater understanding of the extent to which uncertainty in the parameters associated with the anomalous behavior can be narrowed by examining concentration measurements from a network of monitoring wells and to demonstrate the computational speed of the solutions. The developed analytical solutions are encoded and available for use in the open source computational framework MADS (http://mads.lanl.gov). {\textcopyright} 2014 Elsevier Ltd.

Forward model sensitivities are commonly applied to evaluate the uncertainty in model parameter e... more Forward model sensitivities are commonly applied to evaluate the uncertainty in model parameter estimates obtained through inverse analysis. In this case, the forward sensitivity (Jacobian) matrix is applied to compute an approximate representation of the covariance matrix of inverse parameter estimates. However, this approach can produce biased estimates of the covariance matrix because it does not account accurately for correlations between uncertainty of calibration targets and estimates. Typically, these correlations are non-linear and depend on the spatial and temporal structure of inverse targets and estimated parameters. A better but much more computationally intensive method to measure parameter uncertainty, which we call the inverse-sensitivity approach, directly evaluates the sensitivity of inverse estimates of model parameters with respect to the calibration targets. Further, we can evaluate the sensitivity of model predictions based on inverse model parameter estimates with respect to the calibration targets. The proposed methodology can also be applied to problems such as estimation of predictive uncertainty, optimization of data collection strategies, and design of monitoring networks. Its implementation can be performed efficiently through parallelization. Results based on a simple groundwater flow inverse problem are presented to illustrate the basis for the method. {\textcopyright} 2004 Elsevier B.V.

One of the challenges in the design and selection of remediation activities for subsurface contam... more One of the challenges in the design and selection of remediation activities for subsurface contamination is dealing with manifold uncertainties. A scientifically defensible decision process demands consideration of the uncertainties involved. A nonprobabilistic approach based on information gap (info-gap) decision theory is employed to study the robustness of alternative remediation activities. This approach incorporates both parametric and nonparametric (conceptual) uncertainty in predicting contaminant concentrations that are effected by natural processes and the remediation activities. Two remedial scenarios are explored to demonstrate the applicability of the info-gap approach to decision making related to groundwater remediation. Key Points Info-gap decision theory (IGDT) accounts for parametric uncertainty IGDT accounts for nonparametric uncertainty IGDT can be used to choose between different remediation techniques {\textcopyright}2013. American Geophysical Union. All Rights Reserved.

Capture-zone analyses are widely used to facilitate protection of groundwater supplies. Even thou... more Capture-zone analyses are widely used to facilitate protection of groundwater supplies. Even though frequently substantial, transients are commonly ignored in the capture-zone analyses assuming a steady-state flow. Furthermore, advection-only flow paths generally applied in capture-zone analyses might not provide an adequate representation of mean plume behaviour of potential contaminant transport, especially in transient conditions. Here we analyse the impact of the transients and dispersion in the groundwater flow and transport on the capture zone estimates for a series of synthetic cases. Conditions for performing transient advective-dispersive capture-zone analyses are defined. They depend predominantly on the magnitude of groundwater transport velocities at the spatial and temporal scales of interest.

Many of the parameters in subsurface fl ow and transport models cannot be estimated directly at t... more Many of the parameters in subsurface fl ow and transport models cannot be estimated directly at the scale of interest, but can only be derived through inverse modeling. During this process, the parameters are adjusted in such a way that the behavior of the model approximates, as closely and consistently as possible, the observed response of the system under study for some historical period of time. We briefly review the current state of the art of inverse modeling for estimating unsaturated fl ow and transport processes. We summarize how the inverse method works, discuss the historical background that led to the current perspectives on inverse modeling, and review the solution algorithms used to solve the parameter estimation problem. We then highlight our recent work at Los Alamos related to the development and implementa on of improved op miza on and data assimila on methods for computa onally effi cient calibra on and uncertainty es ma on in complex, distributed fl ow and transport models using parallel compu ng capabili es. Finally, we illustrate these develop- ments with three diff erent case studies, including (i) the calibra on of a fully coupled three-dimensional vapor extrac on model using measured concentra ons of vola le organic compounds in the subsurface near the Los Alamos Na onal Laboratory, (ii) the mul objec ve inverse es ma on of soil hydraulic proper es in the HYDRUS-1D model using observed tensiometric data from an experimental fi eld plot in New Zealand, and (iii) the simultaneous es ma on of parameter and states in a groundwater solute mixture model using data from a mul tracer experiment at Yucca Mountain, Nevada.

Care must be taken when choosing a site for geological CO2 sequestration to ensure that the CO2 r... more Care must be taken when choosing a site for geological CO2 sequestration to ensure that the CO2 remains sequestered for many years, and that the environment is not harmed. Making a decision between sites for sequestration is not without its challenges because, as in the case of many subsurface problems, there are a lot of uncertainties. A method for making decisions under various types and severities of uncertainties, Bayesian-Information-Gap Decision Theory (BIG DT), is coupled with a numerical multiphase flow model for CO2 injection. The framework is used to make a decision between two CO2 sequestration sites; data are collected during a test injection and are used by the framework to assess the robustness of each site against failure by either leakage or induced seismic activity. A discussion of how the data are used to decide on a site follows. The results show that at the two synthetic sites examined here, the one with the less leakage potential is preferred. This indicates that the potential for leakage is more prone to violate decision goals at these sites than the potential for overpressurization.

Many of the parameters in subsurface flow and transport models cannot be estimated directly at th... more Many of the parameters in subsurface flow and transport models cannot be estimated directly at the scale of interest, but can only be derived through inverse modeling. During this process, the parameters are adjusted in such a way that the behavior of the model approximates, as closely and consistently as possible, the observed response of the system under study for some historical period of time. We briefly review the current state of the art of inverse modeling for estimating unsaturated flow and transport processes. We summariz how the inverse method works, discuss the historical background that led to the current perspectives on inverse modeling, and review the solution algorithms used to solve the parameter estimation problem. We then highlight our recent work at Los Alamos related to the development and implementation of improved optimization and data assimilation methods for computationally efficient calibration and uncertainty estimation in complex, distributed flow and transport models using parallel computing capabilities. Finally, we illustrate these developments with three different case studies, including (i) the calibration of a fully coupled three-dimensional vapor extraction model using measured concentrations of volatile organic compounds in the subsurface near the Los Alamos National Laboratory, (ii) the multiobjective inverse estimation of soil hydraulic properties in the HYDRUS-1D model using observed tensiometric data from an experimental field plot in New Zealand, and (iii) the simultaneous estimation of parameter and states in a groundwater solute mixture model using data from a multitracer experiment at Yucca Mountain, Nevada. {\textcopyright} Soil Science Society of America. All rights reserved.

$Research paper thumbnail of Analysis of model sensitivity and predictive uncertainty of capture zones in the Espa{\~{n}}ola basin regional aquifer, northern New Mexico$

Predictions and their uncertainty are key aspects of any modelling effort. The prediction uncerta... more Predictions and their uncertainty are key aspects of any modelling effort. The prediction uncertainty can be significant when the predictions depend on uncertain system parameters. We analyse prediction uncertainties through constrained nonlinear second-order optimization of an inverse model. The optimized objective function is the weighted squared difference between observed and simulated system quantities (flux and time-dependent head data). The constraints are defined by the maximization/minimization of the prediction within a given objective function range. The method is applied in capture-zone analyses of groundwater supply systems using a three-dimensional numerical model of the Espa{\~{n}}ola basin aquifer. We use the finite-element simulator, FEHM, coupled with parameter-estimation/predictive-analysis code, PEST. The model is run in parallel on a multi-processor supercomputer. We estimate sensitivity and uncertainty of model predictions, such as capture zone identification and travel times. While the methodology is extremely powerful, it is numerically intensive.

$Research paper thumbnail of Maximum likelihood Bayesian averaging of airflow models in unsaturated fractured tuff using Occam and variance windows$

We use log permeability and porosity data obtained from single-hole pneumatic packer tests in six... more We use log permeability and porosity data obtained from single-hole pneumatic packer tests in six boreholes drilled into unsaturated fractured tuff near Superior, Arizona, to postulate, calibrate and compare five alternative variogram models (exponential, exponential with linear drift, power, truncated power based on exponential modes, and truncated power based on Gaussian modes) of these parameters based on four model selection criteria (AIC, AICc, BIC and KIC). Relying primarily on KIC and cross-validation we select the first three of these variogram models and use them to parameterize log air permeability and porosity across the site via kriging in terms of their values at selected pilot points and at some single-hole measurement locations. For each of the three variogram models we estimate log air permeabilities and porosities at the pilot points by calibrating a finite volume pressure simulator against two cross-hole pressure data sets from sixteen boreholes at the site. The traditional Occam's window approach in conjunction with AIC, AICc, BIC and KIC assigns a posterior probability of nearly 1 to the power model. A recently proposed variance window approach does the same when applied in conjunction with AIC, AICc, BIC but spreads the posterior probability more evenly among the three models when used in conjunction with KIC. We compare the abilities of individual models and MLBMA, based on both Occam and variance windows, to predict space-time pressure variations observed during two cross-hole tests other than those employed for calibration. Individual models with the largest posterior probabilities turned out to be the worst or second worst predictors of pressure in both validation cases. Some individual models predicted pressures more accurately than did MLBMA. MLBMA was far superior to any of the individual models in one validation test and second to last in the other validation test in terms of predictive coverage and log scores. {\textcopyright} 2010 The Author(s).

\textcopyright} 2016 Elsevier LtdEnergy-water nexus has substantially increased importance in the... more \textcopyright} 2016 Elsevier LtdEnergy-water nexus has substantially increased importance in the recent years. Synergistic approaches based on systems-analysis and mathematical models are critical for helping decision makers better understand the interrelationships and tradeoffs between energy and water. In energy-water nexus management, various decision makers with different goals and preferences, which are often conflicting, are involved. These decision makers may have different controlling power over the management objectives and the decisions. They make decisions sequentially from the upper level to the lower level, challenging decision making in energy-water nexus. In order to address such planning issues, a bi-level decision model is developed, which improves upon the existing studies by integration of bi-level programming into energy-water nexus management. The developed model represents a methodological contribution to the challenge of sequential decision-making in energy-water nexus through provision of an integrated modeling framework/tool. An interactive fuzzy optimization methodology is introduced to seek a satisfactory solution to meet the overall satisfaction of the two-level decision makers. The tradeoffs between the two-level decision makers in energy-water nexus management are effectively addressed and quantified. Application of the proposed model to a synthetic example problem has demonstrated its applicability in practical energy-water nexus management. Optimal solutions for electricity generation, fuel supply, water supply including groundwater, surface water and recycled water, capacity expansion of the power plants, and GHG emission control are generated. These analyses are capable of helping decision makers or stakeholders adjust their tolerances to make informed decisions to achieve the overall satisfaction of energy-water nexus management where bi-level sequential decision making process is involved.

The following describes a proposed approach to account for the equifinality of solutions that res... more The following describes a proposed approach to account for the equifinality of solutions that result from comparing observations to flow simulations when using realizations of geostatistical models. We introduce hydrogeological acceptance probability to estimate the propensity of a geostatistical model to produce acceptable realizations with respect to the consistency of their simulations with observations. The estimation of hydrogeological acceptance probability is equivalent to the calculation of the sample mean of a Bernoulli distribution. This allows the estimation of the acceptance probability to be preemptively terminated based on the current estimate and subject to the desired confidence level and interval length. We propose a composite uncertainty analysis of the hydrogeological heterogeneity utilizing acceptable realizations from multiple geostatistical models collected during the estimation of their acceptance probability. In the case of a non-fuzzy definition of realization acceptance, this produces a facies probability map. If the definition of realization acceptance is imprecise, the analysis yields upper and lower bounds on the facies probability map in the form of facies plausibility and belief maps, respectively. These maps can provide indications of the information content of the data and provide guidance for the collection of additional data. ?? 2011 Elsevier Ltd.

$Research paper thumbnail of Three-dimensional numerical inversion of pneumatic cross-hole tests in unsaturated fractured tuff 1. Methodology and borehole effects$

We describe a three-dimensional numerical inverse model for the interpretation of cross-hole pneu... more We describe a three-dimensional numerical inverse model for the interpretation of cross-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site (ALRS) near Superior, Arizona. The model combines a finite volume flow simulator, FEHM, an automatic mesh generator, X3D, a parallelized version of an automatic parameter estimator, PEST, and a geostatistical package, GSTAT. The tests are simulated by considering single-phase airflow through a porous continuum, which represents primarily interconnected fractures at the site. The simulator solves the airflow equations in their original nonlinear form and accounts directly for the ability of all packed-off borehole intervals to store and conduct air through the system. Computations are performed in parallel on a supercomputer using 32 processors. We analyze pneumatic cross-hole test data, previously conducted by our group at ALRS, in two ways: (1) by considering pressure records from individual borehole monitoring intervals one at a time, while treating the rock as being spatially uniform, and (2) by considering pressure records from multiple tests and borehole monitoring intervals simultaneously, while treating the rock as being randomly heterogeneous. The first approach yields a series of equivalent air permeabilities and air-filled porosities for the rock volume being tested, having length scales of the order of meters to tens of meters. The second approach yields a high-resolution geostatistical estimate of how air permeability and air-filled porosity, defined on grid blocks having a length scale of 1 m, vary spatially throughout the tested rock volume. It amounts to three-dimensional pneumatic "tomography" or stochastic imaging of the rock, a concept originally proposed by one of us in 1987. The first paper of this two-part series describes the field data, the model, and the effect of boreholes on pressure propagation through the rock. The second paper implements our approach on selected cross-hole test data from ALRS.

$Research paper thumbnail of Three-dimensional numerical inversion of pneumatic cross-hole tests in unsaturated fractured tuff 2. Equivalent parameters, high-resolution stochastic imaging and scale effects$

In paper 1 of this two-part series we described a three-dimensional numerical inverse model for t... more In paper 1 of this two-part series we described a three-dimensional numerical inverse model for the interpretation of cross-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site (ALRS) near Superior, Arizona. Our model is designed to analyze these data in two ways: (1) by considering pressure records from individual borehole monitoring intervals one at a time, while treating the rock as being spatially uniform, and (2) by considering pressure records from multiple tests and borehole monitoring intervals simultaneously, while treating the rock as being randomly heterogeneous. The first approach yields a series of equivalent air permeabilities and air-filled porosities for rock volumes having length scales ranging from meters to tens of meters, represented nominally by radius vectors extending from injection to monitoring intervals. The second approach yields a high-resolution geostatistical estimate of how air permeability and air-filled porosity, defined on grid blocks having a length scale of 1 m, vary spatially throughout the tested rock volume. It amounts to three-dimensional pneumatic "tomography" or stochastic imaging of the rock. Paper 1 described the field data, the model, and the effect of boreholes on pressure propagation through the rock. This second paper implements our inverse model on pressure data from five cross-hole tests at ALRS. We compare our cross-hole test interpretations by means of the two approaches with earlier interpretations by means of type curves and with geostatistical interpretations of single-hole test data. The comparisons show internal consistency between all pneumatic test interpretations and reveal a very pronounced scale effect in permeability and porosity at ALRS.

$Research paper thumbnail of Bayesian-information-gap decision theory with an application to CO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater} sequestration$

\textcopyright} 2015. American Geophysical Union. All Rights Reserved.Decisions related to subsur... more \textcopyright} 2015. American Geophysical Union. All Rights Reserved.Decisions related to subsurface engineering problems such as groundwater management, fossil fuel production, and geologic carbon sequestration are frequently challenging because of an overabundance of uncertainties (related to conceptualizations, parameters, observations, etc.). Because of the importance of these problems to agriculture, energy, and the climate (respectively), good decisions that are scientifically defensible must be made despite the uncertainties. We describe a general approach to making decisions for challenging problems such as these in the presence of severe uncertainties that combines probabilistic and nonprobabilistic methods. The approach uses Bayesian sampling to assess parametric uncertainty and Information-Gap Decision Theory (IGDT) to address model inadequacy. The combined approach also resolves an issue that frequently arises when applying Bayesian methods to real-world engineering problems related to the enumeration of possible outcomes. In the case of zero nonprobabilistic uncertainty, the method reduces to a Bayesian method. To illustrate the approach, we apply it to a site-selection decision for geologic CO2 sequestration.

\textcopyright} 2014 American Society of Civil Engineers.In contrast to many other engineering fi... more \textcopyright} 2014 American Society of Civil Engineers.In contrast to many other engineering fields, the uncertainties in subsurface processes (e.g., fluid flow and contaminant transport in aquifers) and their parameters are notoriously difficult to observe, measure, and characterize. This causes severe uncertainties that need to be addressed in any decision analysis related to optimal management and remediation of groundwater contamination sites. Furthermore, decision analyses typically rely heavily on complex data analyses and/or model predictions, which are often poorly constrained, as well. Recently, we have developed a model-driven decision support framework (called MADS; http://mads.lanl.gov) for the management and remediation of subsurface contamination sites in which severe uncertainties and complex physics-based models are coupled to perform scientifically defensible decision analyses. The decision analyses are based on Information Gap Decision Theory (IGDT). We demonstrate the MADS capabilities by solving a decision problem related to optimal monitoring network design.

$Research paper thumbnail of Maximum likelihood Bayesian averaging of air flow models in unsaturated fractured tuff$

MLBMA is a maximum likelihood (ML) version of Bayesian model averaging (BMA) that renders it comp... more MLBMA is a maximum likelihood (ML) version of Bayesian model averaging (BMA) that renders it compatible with ML methods of model calibration and thus applicable to cases where prior information about the parameter may be unavailable. We explore the role of prior information in MLBMA by applying it to air flow during a cross-hole pneumatic injection test in unsaturated fractured tuff with and without reliance on packer-test data from six boreholes. We parameterize log air permeability and porosity geostatistically using pilot points and estimate them by calibrating a finite volume pressure simulator (FEHM) against cross-hole pressure data by means of a parallelized version of PEST considering several alternative variogram models. We assess the predictive capabilities of each model based on various model selection criteria and discuss future plans to generate corresponding predictions via MLBMA, cross-validate them against pressure data from the same cross-hole test, and validate them against data from another such test. Copyright {\textcopyright} 2008 IAHS Press.

This paper presents the outcome of a feasibility study on underground coal gasification (UCG) com... more This paper presents the outcome of a feasibility study on underground coal gasification (UCG) combined with direct carbon dioxide (CO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater}) capture and storage (CCS) at a selected site in Bulgaria with deep coal seams ({\textgreater}1,200 m). A series of state-of-the-art geological, geo-mechanical, hydrogeological and computational models supported by experimental tests and techno-economical assessments have been developed for the evaluation of UCG-CCS schemes. Research efforts have been focused on the development of site selection requirements for UCG-CCS, estimation of CO{\textless}inf{\textgreater}2{\textless}/inf{\textgreater} storage volumes, review of the practical engineering requirements for developing a commercial UCG-CCS storage site, consideration of drilling and completion issues, and assessments of economic feasibility and environmental impacts of the scheme. In addition, the risks of subsidence and groundwater contamination have been assessed in order to pave the way for a full-scale trial and commercial applications. The current research confirms that cleaner and cheaper energy with reduced emissions can be achieved and the economics are competitive in the future European energy market. However the current research has established that rigorous design and monitor schemes are essential for productivity and safety and the minimisation of the potential environmental impacts. A platform has been established serving to inform policy-makers and aiding strategies devised to alleviate local and global impacts on climate change, while ensuring that energy resources are optimally harnessed.

$Research paper thumbnail of Numerical inverse interpretation of multistep transient single-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site$

More than 270 single-hole multiple-step air-injection (pneumatic) tests have been conducted by A.... more More than 270 single-hole multiple-step air-injection (pneumatic) tests have been conducted by A.G. Guzman and coworkers in six shallow vertical and slanted boreholes in unsaturated fractured tuffs at the Apache Leap Research Site near Superior, Arizona. Guzman and coworkers used steady-state formulae for single-phase air flow in a uniform, isotropic porous continuum to interpret late data from each step of an injection test. W.A. Illman and coworkers used transient type curves for single-phase air flow in a similar continuum to analyze all data from the first step of several injection tests; however, this analysis did not allow reliable identification of air-filled porosity and the dimensionless borehole storage coefficient. This chapter describes numerical inversion of multistep and recovery data from some of these same tests, based on similar assumptions, by means of a three-dimensional finite-volume code (FEHM) coupled with a parameter-estimation code (PEST). Our numerical inverse model accounts directly for the geometry, flow properties, and storage capabilities of open borehole intervals by treating them as high-permeability and high-porosity cylinders of finite length and radius. It also allows interpreting multiple injection-step and recovery data simultaneously, and yields information about air permeability, air-filled porosity, and the dimensionless borehole storage coefficient. Some of this is difficult to accomplish with the analytical type-curve method. Air permeability values obtained by our inverse method agree well with those obtained by steady-state and type-curve analyses.

$Research paper thumbnail of Three-dimensional inversion of pneumatic tests in fractured rocks$

A three-dimensional geostatistically-based numerical inverse model was developed for the interpre... more A three-dimensional geostatistically-based numerical inverse model was developed for the interpretation of cross-hole pneumatic tests in unsaturated fractured tuffs at the Apache Leap Research Site (ALRS) in Arizona, USA. The model combines a finite-volume flow simulator, FEHM, an automatic mesh generator, X3D, a parallelized version of an automatic parameter estimator, PEST, and a geostatistical code, GSTAT. The tests are simulated by considering singel-phase airflow through an equivalent stochastic porous continuum. The simulator accounts directly for the ability of all packed-off borehole intervals to store and conduct air through the system. Computations are performed in parallel on a supercomputer using 32 processors. We analyze data from several pneumatic cross-hole tests simultaneously to assess the spatial distribution of air permeability and air-filled porosity throughout the test volume. The analysis amounts to three-dimensional pneumatic 'tomography' or stochastic imaging of the rock, a concept originally proposed in connection with hydraulic cross-hole tests in fractured crystalline rocks by Neuman (1987).

$Research paper thumbnail of Numerical inverse interpretation of single-hole pneumatic tests in unsaturated fractured tuff$

A numerical inverse method was used to interpret simultaneously multirate injection and recovery ... more A numerical inverse method was used to interpret simultaneously multirate injection and recovery data from single-hole pneumatic tests in unsaturated fractured tuff at the Apache Leap Research Site near Superior, Arizona. Our model represents faithfully the three-dimensional geometry of boreholes at the site, and accounts directly for their storage and conductance properties by treating them as high-permeability and high-porosity cylinders of finite length and radius. It solves the airflow equations in their original nonlinear form and yields information about air permeability, air-filled porosity and dimensionless borehole storage coefficient. Some of this is difficult to accomplish with analytical type-curves. Air permeability values obtained by our inverse method agree well with those obtained by steady-state and type-curve analyses.

AGU Fall Meeting, San Francisco, CA,, 2014

ZEM ZEM ⇔ MADS LANL Chromium site Highlights ZEM framework ZEM provides automated and reproducibl... more ZEM ZEM ⇔ MADS LANL Chromium site Highlights ZEM framework ZEM provides automated and reproducible workflow interconnecting Data ⇔ Models ⇔ Decisions ZEM is designed for high-performance computing and big-data analysis ZEM employs community software (git/gitlab) for version control, team collaboration and project management using cloud-based repositories (gitlab.com / git.lanl.gov) ⇒ all past model inputs and obtained outputs are stored and can be reproduced ZEM provides quality assurance of the performance assessment process ZEM is written predominantly in : novel high-performance/dynamic language for technical computing (developed at MIT) ZEM ZEM ⇔ MADS LANL Chromium site Highlights Highlights Highlights Highlights Highlights ZEM ZEM ⇔ MADS LANL Chromium site Highlights Interfaces of Data, Models, and Decisions Funded by DOE Office of Science http://dmd.mit.edu ZEM ZEM ⇔ MADS Highlights ZEM provides automated and reproducible workflow interconnecting Data ⇔ Models ⇔ Decisions using high-performance computing and big-data analysis tools ZEM have been successfully applied to perform various data-and model-based analyses at the LANL Chromium site. In the last 3 years, ZEM analyses have accumulated more than 350 CPU-years of wall-clock computational time utilizing simultaneously up to 4096 processors on the LANL HPC clusters ... so far, all the ZEM blind predictions have been consistent with the new observations ZEM ZEM ⇔ MADS LANL Chromium site Highlights Highlights Many uncertainties in the environmental management problems cannot be represented probabilistically Newly developed methodology BIG-DT (Bayesian-Information Gap Decision Theory) is developed to address this issue (O'Malley & Vesselinov 2014 SIAM UQ) BIG-DT is applicable to any real-world engineering problems BIG-DT is available in MADS (open source code written in )