[en] A research and development program was begun three years ago at the Los Alamos Scientific Laboratory (LASL) to study nonmedical applications of computed tomography. This program had several goals. The first goal was to develop the necessary reconstruction algorithms to accurately reconstruct cross sections of nonmedical industrial objects. The second goal was to be able to perform extensive tomographic simulations to determine the efficacy of tomographic reconstruction with a variety of hardware configurations. The final goal was to construct an inexpensive industrial prototype scanner with a high degree of design flexibility. The implementation of these program goals is described

No abstract available

[en] A feasibility study was conducted to determine the possibility of detecting and sizing cracks in reactor cooling water tubes using tomographic techniques. Due to time and financial constraints, only one tomographic reconstruction using the best technique available was made. The results indicate that tomographic reconstructions can, in fact, detect cracks in the tubing and might possibly be capable of measuring the depth of the cracks. Limits of detectability and sensitivity have not been determined but should be investigated in any future work

[en] Additional work has been done to display radiometric data from the Lubbock quadrangle in pseudocolor maps. A digitized topographic map of the quadrangle was obtained from the U.S. Geological Survey and this is being incorporated into the study of the radiometric data. Single-record data from the Lake Mead calibration range and from the Slayton test line have been obtained from GeoMETRICS: Inc. and analysis of this data has begun. Principal component analyses have been used to investigate the relationship of geological formation to the location of points in a (Tl, Bi, K) coordinate system. LASL personnel attended a workshop in Grand Junction, Colorado, where some of the problems of calibrating aerial gamma-ray spectrometers were addressed

[en] Several data-classification schemes were developed by the Los Alamos National Laboratory to detect potential uranium mineralization in the Montrose 1⁰ x 2⁰ quadrangle, Colorado. A first step was to develop and refine the techniques necessary to digitize, integrate, and register various large geological, geochemical, and geophysical data sets, including Landsat 2 imagery, for the Montrose quadrangle, Colorado, using a grid resolution of 1 km. All data sets for the Montrose quadrangle were registered to the Universal Transverse Mercator projection. The data sets include hydrogeochemical and stream sediment analyses for 23 elements, uranium-to-thorium ratios, airborne geophysical survey data, the locations of 90 uranium occurrences, a geologic map and Landsat 2 (bands 4 through 7) imagery. Geochemical samples were collected from 3965 locations in the 19 200 km² quadrangle; aerial data were collected on flight lines flown with 3 to 5 km spacings. These data sets were smoothed by universal kriging and interpolated to a 179 x 119 rectangular grid. A mylar transparency of the geologic map was prepared and digitized. Locations for the known uranium occurrences were also digitized. The Landsat 2 imagery was digitally manipulated and rubber-sheet transformed to quadrangle boundaries and bands 4 through 7 were resampled to both a 1-km and 100-m resolution. All possible combinations of three, for all data sets, were examined for general geologic correlations by utilizing a color microfilm output. Subsets of data were further examined for selected test areas. Two classification schemes for uranium mineralization, based on selected test areas in both the Cochetopa and Marshall Pass uranium districts, are presented. Areas favorable for uranium mineralization, based on these schemes, were identified and are discussed

[en] One objective of the aerial radiometric surveys flown as part of the US Department of Energy's National Uranium Resource Evaluation (NURE) program was to ascertain the regional distribution of near-surface radioelement abundances. Some method for identifying groups of observations with similar radioelement values was therefore required. It is shown in this report that cluster analysis can identify such groups even when no a priori knowledge of the geology of an area exists. A method of convergent k-means cluster analysis coupled with a hierarchical cluster analysis is used to classify 6991 observations (three radiometric variables at each observation location) from the Precambrian rocks of the Copper Mountain, Wyoming, area. Another method, one that combines a principal components analysis with a convergent k-means analysis, is applied to the same data. These two methods are compared with a convergent k-means analysis that utilizes available geologic knowledge. All three methods identify four clusters. Three of the clusters represent background values for the Precambrian rocks of the area, and one represents outliers (anomalously high ₂₁₄Bi). A segmentation of the data corresponding to geologic reality as discovered by other methods has been achieved based solely on analysis of aerial radiometric data. The techniques employed are composites of classical clustering methods designed to handle the special problems presented by large data sets. 20 figures, 7 tables

[en] A research and development program was begun two years ago at the Los Alamos Scientific Laboratory (LASL) to study nonmedical applications of computed tomography. This program had several goals. The first goal was to develop the necessary reconstruction algorithms to accurately reconstruct cross sections of nonmedical industrial objects. The second goal was to be able to perform extensive tomographic simulations to determine the efficacy of tomographic reconstruction with a variety of hardware configurations. The final goal was to construct an inexpensive industrial prototype scanner with a high degree of design flexibility. The implementation of these program goals is described

[en] During the period covered by this report, research was concentrated on multivariate approaches to the analysis of aerial radiometric data. Two aspects of principal components analysis were the subjects of two publications. The procedures recommended for linear discriminant analysis were revised. Progress was made in overlaying LANDSAT data with aerial radiometric data from the Lubbock quadrangle. Some preliminary results from principal components analysis of the Wind River data were obtained

[en] An arbitrary source function cannot be determined fully from projection data that are limited in number and range of viewing angle. There exists a null subspace in the Hilbert space of possible source functions about which the available projection measurements provide no information. The null-space comp]onents of deterministic solutions are usually zero, giving rise to unavoidalbe artifacts. It is demonstrated that these artifacts may be reduced by a Bayesian maximum a posteriori (MAP) reconstruction method that permits the use of significant a priori information. Since normal distributions are assumed for the a priori and measurement-error probability densities, the MAP reconstruction method presented here is equivalent ot the minimum-variance linear estimator with nonstationary mean and covariance ensemble characterizations. A more comprehensive Bayesian approach is suggested in which the ensemble mean and covariance specifications are adjusted on the basis of the measurements

[en] During the period covered by this report, we analyzed the radiometric data collected along the Texas Gulf Coast using ten discriminant analysis techniques to establish radiometric signatures and classify new observations. We conducted a survey of several methods for computing the covariance matrix of large data sets, with particular interest to one-pass algorithms. An investigation of methods of estimating upper-tail percentiles for aerial radiometric data was begun. A feasibility study was conducted concerning the design of ground-based sampling plans using a statistical model for the correlation between observations taken along a flight line. A study of the use of cluster analysis in aerial radiometric data analysis was initiated. Two short courses on statistical methods were presented in Grand Junction, Colorado, and more are planned