Estimating one-dimensional models from frequency-domain electromagnetic data using modular neural networks

Mary M. Poulton, Ralf A. Birken

Research output: Contribution to journalArticle

10 Scopus citations

Abstract

An artificial neural network interpretation system is being used to interpret data from a frequency-domain electromagnetic (EM) geophysical system in near real time. The interpretation system integrates 45 separate networks in a data visualization shell. The networks produce interpretations at three different transmitter-receiver (Tx-Rx) separations for half-space and layered-earth interpretations. Modular neural networks (MNN's) were found to be the only paradigm that could successfully perform the layered-earth interpretations. An MNN with 16 inputs, five local experts, each with seven hidden processing elements, and three outputs was trained on 4795 patterns for 200 epochs. For two-layer models with a resistivity contrast greater than 2:1, resistivity estimates had greater than 96% accuracy for the first-layer resistivity, greater than 98% for the second-layer resistivity, and greater than 96% for the thickness of the first layer. If the contrast is less than 2:1, the resistivity accuracies are unaffected but thickness estimates for layers less than 2 m are unreliable. A Tx-Rx separation of 16 m with maximum depth of penetration of 8 m was assumed for the example cited.

Original languageEnglish (US)
Pages (from-to)547-555
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Volume36
Issue number2
DOIs
StatePublished - Mar 1 1998

ASJC Scopus subject areas

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

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