A GIS System for Inferring Subsurface Geology and Material Properties: Proof of Concept

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.

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Document Details

Document Type
Technical Report
Publication Date
Sep 01, 2006
Accession Number
ADA455819

Entities

People

  • Brian T. Tracy
  • Charles C. Ryerson
  • Judy Ehlen
  • Lawrence W. Gatto
  • Lewis E. Hunter
  • Michael V. Campbell

Tags

Communities of Interest

  • Energy and Power Technologies
  • Sensors
  • Space

DTIC Thesaurus Topics

  • Geographic Information Systems
  • Geography
  • Geology
  • Groundwater
  • Igneous Rocks
  • Information Systems
  • Military Research
  • Photographs
  • Physics Laboratories
  • Ridges
  • Rock Mechanics
  • Terrain
  • Test And Evaluation
  • Three Dimensional
  • Topography
  • Two Dimensional
  • Water Resources

Fields of Study

  • Geology

Readers

  • Computational Modeling and Simulation
  • Computer Vision.
  • Geotechnical Engineering.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference