Hopfield neural network for seismic horizon picking

Kou-Yuan Huang*

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

6 Scopus citations

Abstract

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 horn 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.

Original languageEnglish
Pages562-565
Number of pages4
DOIs
StatePublished - 1 Jan 1997
Event1997 Society of Exploration Geophysicists Annual Meeting, SEG 1997 - Dallas, United States
Duration: 2 Nov 19977 Nov 1997

Conference

Conference1997 Society of Exploration Geophysicists Annual Meeting, SEG 1997
CountryUnited States
CityDallas
Period2/11/977/11/97

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