Seismic wavelet extraction using artificial neural networks
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Geophysical events are of interest to the interpreter as an indicator of geological boundaries and structures. In structural analysis, extraction of reflection events is still commonly done by hand, a process which is error-prone and time consuming. Attempts to automate the process are hindered by the absence of a clear, robust and universal picking algorithm. A new feature extraction technique for seismic data interpretation, using a trained Artificial Neural Network is presented. It is shown that this method is useful in extracting geophysical events where conventional pattern recognition techniques may fail.