The Remote Geologic Analysis (RGA) system was developed by Pacific Northwest Laboratory (PNL) to identify crustal structures that may affect seismic wave propagaUon from nuclear tests. Using automated methods, the RGA system identifies all valleys in a digital elevation model (DEM), fits three-dimensional vectors to valley bottoms, and catalogs all potential fracture or fault planes defined by coplanar pairs of valley vectors. The system generates a cluster hierarchy of planar features having greater-than-random density that may represent areas of anomalous to-" pography manifesting structural control of erosional drainage development. Because RGA uses computer methods to identify zones of hypothesized structural control of topography, ground truth using a weU-characterized test site was critical in our evaluation of RGA's characterization of inaccessible test sites for seismic verification studies. Therefore, we applied RGA to a study area centered on Yucca Flat at the Nevada Test Site (NTS) and compared our results with both mapped geology and geologic structures and with seismic yield-magnitude models. This is the final report of PNL's RGA developmem project for peer review within the U.S. Department of Energy Office of Arms Control (OAC) seismic-verification coL_-nunity. In this report, we discuss the Yucca Flat study area, the analytical basis of the RGA system and its application to Yucca Flat, the results of the analysis, and the relation of the analytical results to known topography, geology, and geologic structures.Our analysis consisted of three parts.The first part was a geomorphic pattern-recognition analysis in which we applied a valley-finder algorithm, a vector-flttir_g algorithm, and coplanar analysis to the entire Yucca Flat DEM to identify coplanar valley segments that may be evidence of erosional control by planar fracture zones. The RGA algorithms generated 26,473 planes from the Yucca Flat area of the NTS. In the second part, we used statistical cluster analysis to locate zones of coplanar valley segments that were of anomalously high density (compared with random associations), which were the final output of the remote analysis. We derived several thousand clusters containing as many as 2,351 planes. From these, we picked the most significant clusters based on statistical and geologic criteriadeveloped in previous test applications; the result was 21 clusters to be evaluated with geologic and geophysical data from Yucca Flat. In the third part, we compared output of the remote analysis with topographic, geologic, and geophysical features to evaluate structural significance.By comparing these clusters with topographic, geologic, and geophysical data for Yucca Flat, we concluded that H_ accurately detects vaneys in the DEM, performs accurate coplanar correlations, and isolates statistically meaningful plane clusters that can be ranked in order of significance. While segments of RGA plane clusters correlate with numerous mapped faults, no sini, gle fault or associations of fault zones seem to ...