GIS and remote sensing data for allowing detection of structural features, such as faults, offer opportunities for improving mapping and identifying the areas that are likely to be locations of faulting areas. Landsat ETM-7 satellite data images were used and band-5 was found as the most suitable band for lineament delineation, based on the ability to identify geological features. Four contributing factors, namely, drainage patterns, faults (previously mapped), lineaments, and lithological contacts layers, were parameters used in this study to produce a fault potential prediction map using the overlay model techniques. The potential map (fault susceptibility map) classifies the study area into five potential zones, namely, very low, low, moderate, high, and very high potential. The areas covered by moderate to the highest potential zones were considered as fault segments (fault lines) in the area. The comparison of the potential map and the published fault map by using GIS matching techniques shows that 75 fault segments (fault lines) in the potential map were not properly identified in the study area. The correlation between fault segments and faults data collected from field work stations shows that there were 39 fault segments which may represent new faults in the area being identified. The presence of these faults is not known from the literature; this leads to updating and revising of existing geological map of the study area.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.