HR: 14:25h
AN: U23C-04    [Abstracts]
TI: Quantification of Reflection Patterns in Ground-Penetrating Radar Data
AU: * Moysey, S
EM: smoysey@clemson.edu
AF: Clemson University, School of the Environment, Clemson, SC 29634 United States
AU: Knight, R J
EM: rknight@stanford.edu
AF: Stanford University, Dept. of Geophysics, Stanford, CA 94305 United States
AU: Jol, H M
EM: jolhm@uwec.edu
AF: University of Wisconsin-Eau Claire, Dept. of Geography, Eau Claire, WI 54702 United States
AU: Allen-King, R M
EM: richelle@geology.buffalo.edu
AF: SUNY at Buffalo, Dept. of Geology, Buffalo, NY 14260 United States
AU: Gaylord, D R
EM: gaylordd@wsu.edu
AF: Washington State University, Dept. of Geology, Pullman, WA 99164 United States
AB: Radar facies analysis provides a way of interpreting the large-scale structure of the subsurface from ground-penetrating radar (GPR) data. Radar facies are often distinguished from each other by the presence of patterns, such as flat-lying, dipping, or chaotic reflections, in different regions of a radar image. When these patterns can be associated with radar facies in a repeated and predictable manner we refer to them as `radar textures'. While it is often possible to qualitatively differentiate between radar textures visually, pattern recognition tools, like neural networks, require a quantitative measure to discriminate between them. We investigate whether currently available tools, such as instantaneous attributes or metrics adapted from standard texture analysis techniques, can be used to improve the classification of radar facies. To this end, we use a neural network to perform cross-validation tests that assess the efficacy of different textural measures for classifying radar facies in GPR data collected from the William River delta, Saskatchewan, Canada. We found that the highest classification accuracies (>93%) were obtained for measures of texture that preserve information about the spatial arrangement of reflections in the radar image, e.g., spatial covariance. Lower accuracy (87%) was obtained for classifications based directly on windows of amplitude data extracted from the radar image. Measures that did not account for the spatial arrangement of reflections in the image, e.g., instantaneous attributes and amplitude variance, yielded classification accuracies of less than 65%. Optimal classifications were obtained for textural measures that extracted sufficient information from the radar data to discriminate between radar facies but were insensitive to other facies specific characteristics. For example, the rotationally invariant Fourier-Mellin transform delivered better classification results than the spatial covariance because dip angle of the reflections, but not dip direction, was an important discriminator between radar facies at the William River delta. To extend the use of radar texture beyond the identification of radar facies to sedimentary facies we are investigating how sedimentary features are encoded in GPR data at Borden, Ontario, Canada. At this site, we have collected extensive sedimentary and hydrologic data over the area imaged by GPR. Analysis of this data coupled with synthetic modeling of the radar signal has allowed us to develop insight into the generation of radar texture in complex geologic environments.
DE: 0520 Data analysis: algorithms and implementation
DE: 0540 Image processing
DE: 0555 Neural networks, fuzzy logic, machine learning
DE: 0669 Scattering and diffraction
DE: 0689 Wave propagation (2487, 3285, 4275, 4455, 6934)
SC: Union [U]
MN: Fall Meeting 2005