Neural network for seismic horizon picking

Kou-Yuan Huang*

*Corresponding author for this work

研究成果: Paper同行評審

3 引文 斯高帕斯(Scopus)

摘要

Hopfield neural network can solve the optimization problem. We use Hopfield net to the seismic horizon picking. The peak position of each seismic wavelet is corresponding to one neuron. We transform the constraints of the detecting local horizon patterns and the constraints of extracting one horizon each time into the system energy function. From the theory of Hopfield net, changing the values of neurons can decrease the energy. The system will be stable until the values of neurons are not changed. One horizon is extracted by using the algorithm at each time. Remove the extracted horizon from the original seismic data and extract the next horizon until the last horizon is extracted. From the experimental results in bright spot, the picked horizons can match the visual inspection.

原文English
頁面1840-1844
頁數5
DOIs
出版狀態Published - 1 一月 1998
事件Proceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3) - Anchorage, AK, USA
持續時間: 4 五月 19989 五月 1998

Conference

ConferenceProceedings of the 1998 IEEE International Joint Conference on Neural Networks. Part 1 (of 3)
城市Anchorage, AK, USA
期間4/05/989/05/98

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