Self-organizing neural network for picking seismic horizons

Kou-Yuan Huang*, William R.I. Chang, H. T. Yen

*Corresponding author for this work

研究成果: Paper同行評審

9 引文 斯高帕斯(Scopus)


Applications of self-organizing feature maps to various cognitive tasks have been demonstrated by Kohonen. In this paper, we design an algorithm based on self-organization model with a linear array feature maps to pick the seismic horizons in the seismogram. This algorithm creats a vector quantizer by adjusting weighting vectors from the input-vectors. Weights are initially set to snail random values. From the property of seismic horizon, a set of neurons that joined together in a one dimensional linear array is used. In the self-organizing process, the distribution of weights is tending into an approximate form which best imitates the structure of the input density. We have applied the algorithm on simulated and real seismograms. This self-organizing neural network technique can be used as a pre-processing of the seismic pattern recognition and improve the seismic interpretations.

出版狀態Published - 1 一月 1990
事件1990 Society of Exploration Geophysicists Annual Meeting - San Francisco, United States
持續時間: 23 九月 199027 九月 1990


Conference1990 Society of Exploration Geophysicists Annual Meeting
國家United States
城市San Francisco

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