Neural network based layer picking for unfocused resistivity log parameterization

Lin Zhang, Mary Poulton, Alberto Mezzatesta

Research output: Contribution to conferencePaper

1 Scopus citations

Abstract

Layer boundary determination is important for earth model parameterization in inversion methods. Unfocused logging tools can produce responses that are asymmetric with respect to layer boundaries. This means we cannot extract accurate layer boundaries based on breaks in the slope. We have developed a neural network based approach to pick layer boundaries from unfocused log data by correcting for the shifted response. The neural network approach to layer picking provides a general way to solve the problem of layer boundary determination and avoids the need for a multitude of specialized algorithms for specific situations or tools.

Original languageEnglish (US)
StatePublished - Jan 1 1999
Event1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999 - Houston, United States
Duration: Oct 31 1999Nov 5 1999

Other

Other1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999
CountryUnited States
CityHouston
Period10/31/9911/5/99

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ASJC Scopus subject areas

  • Geophysics

Cite this

Zhang, L., Poulton, M., & Mezzatesta, A. (1999). Neural network based layer picking for unfocused resistivity log parameterization. Paper presented at 1999 Society of Exploration Geophysicists Annual Meeting, SEG 1999, Houston, United States.