The morphology and prevalence of macropores < 10 cm in diameter in forested riparian wetlands is largely unknown despite their importance as a mechanism for preferential flow of contaminants to stream channels. Here, we validate field procedures for detecting and mapping the three‐dimensional structure of near‐surface (15–65 cm deep) lateral macropore networks using non‐invasive ground‐penetrating radar (GPR) technology at a Mid‐Atlantic riparian wetland field study site. Soil core samples used to ground truth the procedures showed that the detection predictions were 92% accurate and tracer dye transmission through the site corroborated the morphology predictions. The results demonstrate the feasibility of using GPR to map preferential flow networks in situ without disturbing environmentally sensitive wetland ecosystems.
Abstract : This study has been devoted to extension and further development of search algorithms of utility for self-organizing control systems. The four major results of this study are as follows: Self-organizing search methods developed in the previous study (AMRL-TR-73-76) have been extended to higher- dimensional multi-modal problems and have been shown to be very effective. A composite search algorithm incorporating both the pdf-guided search and the guided accelerated random search was found to be more effective than either search algorithm alone. Clustering analysis has been shown to be a valuable tool for assessing the complexity of a search surface. The number of modes (peaks), their locations relative to each other, their shape and volume, and the estimated maximum performance value within each are all adaptively determined via clustering. A new method for image encoding has been formulated that provides image reconstruction of similar quality to methods currently in use. This procedure also can find regions of possible interest within the image because of its ability to treat the image as a whole rather than line-by-line. This characteristic considerably enhances its value as a tool in image pattern recognition and classification.
Umambiguous discrimination and accurate sizing between simulated pits and cracks have been obtained via Adaptive Learning Network (ANL) flaw-classification and ALN flaw-size models for both single and multiple frequency eddy current data. In terms of sizing flaws, the error rates were 2.4 percent for pits and 3.6 percent for cracks. Eddy current signal responses were generated, recorded, and digitized from several simulated pits and cracks in sample nuclear reactor steam generator tubing. These responses were parameterized to measure the in-phase and quadrature signal and power components. The accuracy of ALN flaw classifiers and ALN flaw-size models, which were synthesized from these parameters, was independent of the presence of tube-support plates over the flaw region. It is concluded from this feasibility study, which considered 100, 200, 300 and 400 kHz signals, that the optimum inspection mode for pits is with a single frequency (400 kHz) eddy current carrier signal and the optimum inspection mode for cracks is with multiple frequencies (200 kHz and 400 kHz).
