Publications

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The Bio-Restoration of Major Transportation Facilities Domestic Demonstration and Application Program (DDAP) is a designed to accelerate the restoration of transportation nodes following an attack with a biological warfare agent. This report documents the technology development work done at SNL for this DDAP, which include development of the BROOM tool, an investigation of surface sample collection efficiency, and a flow cytometry study of chlorine dioxide effects on Bacillus anthracis spore viability.

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In February of 2005, a joint exercise involving Sandia National Laboratories (SNL) and the National Institute for Occupational Safety and Health (NIOSH) was conducted in Albuquerque, NM. The SNL participants included the team developing the Building Restoration Operations and Optimization Model (BROOM), a software product developed to expedite sampling and data management activities applicable to facility restoration following a biological contamination event. Integrated data-collection, data-management, and visualization software improve the efficiency of cleanup, minimize facility downtime, and provide a transparent basis for reopening. The exercise was held at an SNL facility, the Coronado Club, a now-closed social club for Sandia employees located on Kirtland Air Force Base. Both NIOSH and SNL had specific objectives for the exercise, and all objectives were met.

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Efficient and reliable unexploded ordnance (UXO) site characterization is needed for decisions regarding future land use. There are several types of data available at UXO sites and geophysical signal maps are one of the most valuable sources of information. Incorporation of such information into site characterization requires a flexible and reliable methodology. Geostatistics allows one to account for exhaustive secondary information (i.e.,, known at every location within the field) in many different ways. Kriging and logistic regression were combined to map the probability of occurrence of at least one geophysical anomaly of interest, such as UXO, from a limited number of indicator data. Logistic regression is used to derive the trend from a geophysical signal map, and kriged residuals are added to the trend to estimate the probabilities of the presence of UXO at unsampled locations (simple kriging with varying local means or SKlm). Each location is identified for further remedial action if the estimated probability is greater than a given threshold. The technique is illustrated using a hypothetical UXO site generated by a UXO simulator, and a corresponding geophysical signal map. Indicator data are collected along two transects located within the site. Classification performances are then assessed by computing proportions of correct classification, false positive, false negative, and Kappa statistics. Two common approaches, one of which does not take any secondary information into account (ordinary indicator kriging) and a variant of common cokriging (collocated cokriging), were used for comparison purposes. Results indicate that accounting for exhaustive secondary information improves the overall characterization of UXO sites if an appropriate methodology, SKlm in this case, is used. © Springer Science+Business Media, Inc. 2005.

Real-time water quality and chemical-specific sensors are becoming more commonplace in water distribution systems. The overall objective of the sensor network is to protect consumers from accidental and malevolent contamination events occurring within the distribution network. This objective can be quantified several different ways including: minimizing the amount of contaminated water consumed, minimizing the extent of the contamination within the network, minimizing the time to detection, etc. We examine the ability of a sensor network to meet these objectives as a function of both the detection limit of the sensors and the number of sensors in the network. A moderately-sized network is used as an example and sensors are placed randomly. The source term is a passive injection into a node and the resulting concentration in the node is a function of the volumetric flow through that node. The concentration of the contaminant at the source node is averaged for all time steps during the injection period. For each combination of a certain number of sensors and a detection limit, the mean values of the different objectives across multiple random sensor placements are evaluated. Results of this analysis allow the tradeoff between the necessary detection limit in a sensor and the number of sensors to be evaluated. Results show that for the example problem examined here, a sensor detection limit of 0.01 of the average source concentration is adequate for maximum protection.

Sandia National Laboratories, under contract to Nuclear Waste Management Organization of Japan (NUMO), is performing research on regional classification of given sites in Japan with respect to potential volcanic disruption using multivariate statistics and geo-statistical interpolation techniques. This report provides results obtained for hierarchical probabilistic regionalization of volcanism for the Sengan region in Japan by applying multivariate statistical techniques and geostatistical interpolation techniques on the geologic data provided by NUMO. A workshop report produced in September 2003 by Sandia National Laboratories (Arnold et al., 2003) on volcanism lists a set of most important geologic variables as well as some secondary information related to volcanism. Geologic data extracted for the Sengan region in Japan from the data provided by NUMO revealed that data are not available at the same locations for all the important geologic variables. In other words, the geologic variable vectors were found to be incomplete spatially. However, it is necessary to have complete geologic variable vectors to perform multivariate statistical analyses. As a first step towards constructing complete geologic variable vectors, the Universal Transverse Mercator (UTM) zone 54 projected coordinate system and a 1 km square regular grid system were selected. The data available for each geologic variable on a geographic coordinate system were transferred to the aforementioned grid system. Also the recorded data on volcanic activity for Sengan region were produced on the same grid system. Each geologic variable map was compared with the recorded volcanic activity map to determine the geologic variables that are most important for volcanism. In the regionalized classification procedure, this step is known as the variable selection step. The following variables were determined as most important for volcanism: geothermal gradient, groundwater temperature, heat discharge, groundwater pH value, presence of volcanic rocks and presence of hydrothermal alteration. Data available for each of these important geologic variables were used to perform directional variogram modeling and kriging to estimate values for each variable at 23949 centers of the chosen 1 km cell grid system that represents the Sengan region. These values formed complete geologic variable vectors at each of the 23,949 one km cell centers.

Multivariate spatial classification schemes such as regionalized classification or principal components analysis combined with kriging rely on all variables being collocated at the sample locations. In these approaches, classification of the multivariate data into a finite number of groups is done prior to the spatial estimation. However, in some cases, the variables may be sampled at different locations with the extreme case being complete heterotopy of the data set. In these situations, it is necessary to adapt existing techniques to work with non-collocated data. Two approaches are considered: (1) kriging of existing data onto a series of 'collection points' where the classification into groups is completed and a measure of the degree of group membership is kriged to all other locations; and (2) independent kriging of all attributes to all locations after which the classification is done at each location. Calculations are conducted using an existing groundwater chemistry data set in the upper Dakota aquifer in Kansas (USA) and previously examined using regionalized classification (Bohling, 1997). This data set has all variables measured at all locations. To test the ability of the first approach for dealing with non-collocated data, each variable is reestimated at each sample location through a cross-validation process and the reestimated values are then used in the regionalized classification. The second approach for non-collocated data requires independent kriging of each attribute across the entire domain prior to classification. Hierarchical and non-hierarchical classification of all vectors is completed and a computationally less burdensome classification approach, 'sequential discrimination', is developed that constrains the classified vectors to be chosen from those with a minimal multivariate kriging variance. Resulting classification and uncertainty maps are compared between all non-collocated approaches as well as to the original collocated approach. The non-collocated approaches lead to significantly different group definitions compared to the collocated case. To some extent, these differences can be explained by the kriging variance of the estimated variables. Sequential discrimination of locations with a minimum multivariate kriging variance constraint produces slightly improved results relative to the collection point and the non-hierarchical classification of the estimated vectors.

Preliminary investigation areas (PIA) for a potential repository of high-level radioactive waste must be evaluated by NUMO with regard to a number of qualifying factors. One of these factors is related to earthquakes and fault activity. This study develops a spatial statistical assessment method that can be applied to the active faults in Japan to perform such screening evaluations. This analysis uses the distribution of seismicity near faults to define the width of the associated process zone. This concept is based on previous observations of aftershock earthquakes clustered near active faults and on the assumption that such seismic activity is indicative of fracturing and associated impacts on bedrock integrity. Preliminary analyses of aggregate data for all of Japan confirmed that the frequency of earthquakes is higher near active faults. Data used in the analysis were obtained from NUMO and consist of three primary sources: (1) active fault attributes compiled in a spreadsheet, (2) earthquake hypocenter data, and (3) active fault locations. Examination of these data revealed several limitations with regard to the ability to associate fault attributes from the spreadsheet to locations of individual fault trace segments. In particular, there was no direct link between attributes of the active faults in the spreadsheet and the active fault locations in the GIS database. In addition, the hypocenter location resolution in the pre-1983 data was less accurate than for later data. These pre-1983 hypocenters were eliminated from further analysis.

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This report describes the methodology and results of a project to develop a neural network for the prediction of the measured hydraulic conductivity or transmissivity in a series of boreholes at the Tono, Japan study site. Geophysical measurements were used as the input to EL feed-forward neural network. A simple genetic algorithm was used to evolve the architecture and parameters of the neural network in conjunction with an optimal subset of geophysical measurements for the prediction of hydraulic conductivity. The first attempt was focused on the estimation of the class of the hydraulic conductivity, high, medium or low, from the geophysical logs. This estimation was done while using the genetic algorithm to simultaneously determine which geophysical logs were the most important and optimizing the architecture of the neural network. Initial results showed that certain geophysical logs provided more information than others- most notably the 'short-normal', micro-resistivity, porosity and sonic logs provided the most information on hydraulic conductivity. The neural network produced excellent training results with accuracy of 90 percent or greater, but was unable to produce accurate predictions of the hydraulic conductivity class. The second attempt at prediction was done using a new methodology and a modified data set. The new methodology builds on the results of the first attempts at prediction by limiting the choices of geophysical logs to only those that provide significant information. Additionally, this second attempt uses a modified data set and predicts transmissivity instead of hydraulic conductivity. Results of these simulations indicate that the most informative geophysical measurements for the prediction of transmissivity are depth and sonic log. The long normal resistivity and self potential borehole logs are moderately informative. In addition, it was found that porosity and crack counts (clear, open, or hairline) do not inform predictions of hydraulic transmissivity.

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