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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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