Please use this identifier to cite or link to this item: https://hdl.handle.net/11681/8495
Title: A GIS system for inferring subsurface geology and material properties : proof of concept
Authors: United States. Army. Corps of Engineers. Sacramento District.
U.S. Army Topographic Engineering Center.
Gatto, Lawrence W.
Campbell, Michael V.
Ehlen, Judy, 1944-
Ryerson, Charles C. (Charles Curtis)
Hunter, Lewis E.
Tracy, Brian T.
Keywords: Geographic information systems
GIS
Geologic analysis
Material properties
Terrain analysis
Geological mapping
Issue Date: Sep-2006
Publisher: Cold Regions Research and Engineering Laboratory (U.S.)
Engineer Research and Development Center (U.S.)
Series/Report no.: ERDC TR ; 06-6.
Description: Technical Report
Abstract: This report describes the concept for a geographical information system (GIS) that can infer subsurface geology and material properties. The hypotheses were that a GIS can be programmed to (1.) follow the fundamental logic sequence developed for traditional terrain- and image-analysis procedures to infer geologic materials; (2.) augment that sequence with correlative geospatial data from a variety of sources; and (3.) integrate the inferences and data to develop “best-guess” estimates. Structured logic trees were developed to guide a terrain analyst through an interactive, geologic analysis based on querying and mentoring logic primarily using imagery and map data as input. The logic trees allow a terrain analyst with limited geology background and experience to rapidly infer the most likely geologic material. A new surface projection method was also developed to estimate depth to bedrock, and an existing method to determine depth to the water table was significantly expanded. The concept was proven to be feasible during blind evaluations conducted at Camp Grayling, MI, a cool, temperate, vegetation-covered site, and at Yuma Proving Ground, AZ, and Fort Irwin, CA, both hot, arid, barren sites. The results show that an analyst can infer the correct geologic conditions 70–80% of the time using these inferential methods.
URI: http://hdl.handle.net/11681/8495
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