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

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2 Author(s)
M. M. Poulton ; Dept. of Min. & Geol. Eng., Arizona Univ., Tucson, AZ, USA ; R. A. Birken

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 (MNNs) 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

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:36 ,  Issue: 2 )