Geology topics

A.J. Graettinger

Publications and source records attributed to A.J. Graettinger.

4 recordsLinked to original sources

USGS2009

Directed site exploration for permeable reactive barrier design

Permeable reactive barriers (PRBs) are being employed for in situ site remediation of groundwater that is typically flowing under natural gradients. Site characterization is of critical importance to the success of a PRB. A design-specific site exploration approach called quantitatively directed exploration (QDE) is presented. The QDE approach employs three spatially related matrices: (1) covariance of input parameters, (2) sensitivity of model outputs, and (3) covariance of model outputs to identify the most important location to explore based on a specific design. Sampling at the location that most reduces overall site uncertainty produces a higher probability of success of a particular design. The QDE approach is demonstrated on the Kansas City Plant, Kansas City, MO, a case study where a PRB was installed and failed. It is shown that additional quantitatively directed site exploration during the design phase could have prevented the remedial failure that was caused by missing a geologic body having high hydraulic conductivity at the south end of the barrier. The most contributing input parameter approach using head uncertainty clearly indicated where the next sampling should be made toward the high hydraulic conductivity zone. This case study demonstrates the need to include the specific design as well as site characterization uncertainty when choosing the sampling locations. ?? 2008 Elsevier B.V.

Journal of Hazardous Materials↗

USGS2006

Quantitative methods to direct exploration based on hydrogeologic information

Quantitatively Directed Exploration (QDE) approaches based on information such as model sensitivity, input data covariance and model output covariance are presented. Seven approaches for directing exploration are developed, applied, and evaluated on a synthetic hydrogeologic site. The QDE approaches evaluate input information uncertainty, subsurface model sensitivity and, most importantly, output covariance to identify the next location to sample. Spatial input parameter values and covariances are calculated with the multivariate conditional probability calculation from a limited number of samples. A variogram structure is used during data extrapolation to describe the spatial continuity, or correlation, of subsurface information. Model sensitivity can be determined by perturbing input data and evaluating output response or, as in this work, sensitivities can be programmed directly into an analysis model. Output covariance is calculated by the First-Order Second Moment (FOSM) method, which combines the covariance of input information with model sensitivity. A groundwater flow example, modeled in MODFLOW-2000, is chosen to demonstrate the seven QDE approaches. MODFLOW-2000 is used to obtain the piezometric head and the model sensitivity simultaneously. The seven QDE approaches are evaluated based on the accuracy of the modeled piezometric head after information from a QDE sample is added. For the synthetic site used in this study, the QDE approach that identifies the location of hydraulic conductivity that contributes the most to the overall piezometric head variance proved to be the best method to quantitatively direct exploration. ?? IWA Publishing 2006.

Journal of Hydroinformatics↗

USGS2005

MODFLOW 2000 Head Uncertainty, a First-Order Second Moment Method

A computationally efficient method to estimate the variance and covariance in piezometric head results computed through MODFLOW 2000 using a first-order second moment (FOSM) approach is presented. This methodology employs a first-order Taylor series expansion to combine model sensitivity with uncertainty in geologic data. MODFLOW 2000 is used to calculate both the ground water head and the sensitivity of head to changes in input data. From a limited number of samples, geologic data are extrapolated and their associated uncertainties are computed through a conditional probability calculation. Combining the spatially related sensitivity and input uncertainty produces the variance-covariance matrix, the diagonal of which is used to yield the standard deviation in MODFLOW 2000 head. The variance in piezometric head can be used for calibrating the model, estimating confidence intervals, directing exploration, and evaluating the reliability of a design. A case study illustrates the approach, where aquifer transmissivity is the spatially related uncertain geologic input data. The FOSM methodology is shown to be applicable for calculating output uncertainty for (1) spatially related input and output data, and (2) multiple input parameters (transmissivity and recharge).

Ground Water↗

USGS2003

Use of input uncertainty and model sensitivity to guide site exploration

Three Quantitatively Directed Exploration (QDE) methods to identify optimum field sampling locations based on model input covariance and model sensitivity are presented. The first method bases site exploration only on the spatial variation in the uncertainty of input properties. The second method uses only the spatial variation in model sensitivities. The third method uses a first-order second-moment (FOSM) method to estimate the spatial variation in the output covariance. The FOSM method estimates output uncertainty using the product of the input covariance and model sensitivity. The three methods are illustrated by means of a synthetic groundwater site simulated with MODFLOW-2000. The groundwater-flow model computes piezometric head and the sensitivity of head to changes in input values. The QDE methods are evaluated by comparing model results to the "true" head. For the synthetic site used in this study, the most effective QDE method was the FOSM method.

Conference Paper↗