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Book Chapter

8: First Break Picking Using a Neural Network

By
Veezhinathan Jay
Veezhinathan Jay
Amoco Production Company, Tulsa Research Center, 4502 East 41st Street, Tulsa, OK. 74102.
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Wagner Don
Wagner Don
Amoco Production Company, Tulsa Research Center, 4502 East 41st Street, Tulsa, OK. 74102.
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Ehlers Jerry
Ehlers Jerry
Amoco Production Company, Tulsa Research Center, 4502 East 41st Street, Tulsa, OK. 74102.
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Published:
January 01, 1991

Abstract

Traditional von Neumann computers using traditional programs are extremely good at number crunching tasks, but they cannot approach human performance in simple perceptual tasks such as recognizing a face or identifying a sound. This discrepancy, among other things, has been a major motivating factor in developing brain-based, massively parallel computing architectures. The neural net paradigm is one such paradigm that has proved to be good at pattern recognition tasks.

In exploration geophysics, the picking of seismic first arrivals represents a pattern recognition task. We attacked this problem by defining four signal attributes for each potential first arrival peak as input to a back propagation neural network. Then, by using a set of known (user selected) first arrival peaks, we trained the back propagation network to recognize first arrival peaks. The neural net based first arrival picking system achieved above 90 percent accuracy on picking several seismic surveys (each survey required a separate training).

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Contents

Geophysical Developments Series

Expert Systems in Exploration

Fred Aminzadeh
Fred Aminzadeh
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Marwan Simaan
Marwan Simaan
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Society of Exploration Geophysicists
Volume
3
ISBN electronic:
9781560802532
Publication date:
January 01, 1991

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