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Interpretation of Shallow Stratigraphic Facies Using a Self-Organizing Neural Network

  • Curtis A. Link
  • Stuart Blundell
Part of the Modern Approaches in Geophysics book series (MAGE, volume 21)

Abstract

A study was recently conducted to assess the extent of hydrocarbon impacts to groundwater and soil resources at a regional petroleum refinery. To accomplish the study, 46 groundwater-monitoring wells were installed at the site. Data collected from the wells included detailed lithologic descriptions from samples and cuttings, and suites of geophysical well logs. Because the quality of the lithologic descriptions was erratic, our approach was to produce lithofacies interpretations based on gamma ray logs, used as input to a neural network classifier system.

Keywords

Output Class Smoothing Filter Unify Soil Classification System Neutron Porosity Gravel Unit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. Hagan M. T., Demuth, H. B., and Beale, M., 1995, Neural network design: PWS Publishing Co.Google Scholar
  2. Kohonen, T., 1987, Self-organization and associative memory, 2“d Ed.: Springer-Verlag.Google Scholar
  3. Matlab Neural Network Toolbox User’s Guide, 1993: The MathWorks, Inc.Google Scholar

Copyright information

© Springer Science+Business Media Dordrecht 2003

Authors and Affiliations

  • Curtis A. Link
    • 1
  • Stuart Blundell
    • 2
  1. 1.Department of Geophysical EngineeringMontana Tech of the University of MontanaButteUSA
  2. 2.Integrated Geoscience Inc.HelenaUSA

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