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- W2085328075 abstract "The Artificial Neural Networks discussed to this date in the geophysical literature have been single trace processing tools. We show that significant improvements can be realized by incorporating a variety of multi-trace prediction criteria into the neural network. The expansion of the network into the multi-trace realm enables it to “see” the ensemble or stack as a single entity, rather than as an isolated series of traces. The robustness of the tool is enhanced by the feature that allows the human processor to add control points, correct, or retrain the network during the application phase until the network performs satisfactorily. We use a cascade-correlation network whose inherent speed and incremental learning ability make it possible with most data sets to train and apply the network interactively. Examples of the effectiveness of the interactive multitrace technique are shown for first break picking and for post-stack horizon recognition." @default.
- W2085328075 created "2016-06-24" @default.
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- W2085328075 date "1993-01-01" @default.
- W2085328075 modified "2023-09-24" @default.
- W2085328075 title "Toward more robust neural‐network first break and horizon pickers" @default.
- W2085328075 cites W2078496737 @default.
- W2085328075 doi "https://doi.org/10.1190/1.1822449" @default.
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