Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

Shear wave velocity prediction based on 1DCNN-BiLSTM network with attention mechanism

The Shear wave (S-wave) velocity is an essential parameter in reservoir characterization and evaluation, fluid identification, and prestack inversion. However, the cost of obtaining S-wave velocities directly from dipole acoustic logging is relatively high. At the same time, conventional data-driven...

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Bibliographic Details
Main Authors: Gang Feng, Wen-Qing Liu, Zhe Yang, Wei Yang
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-04-01
Series:Frontiers in Earth Science
Subjects:
shear wave velocity prediction
well logging data
convolution neural network
bidirectional long short-term memory
attention mechanism
deep learning
Online Access:https://www.frontiersin.org/articles/10.3389/feart.2024.1376344/full

Internet

https://www.frontiersin.org/articles/10.3389/feart.2024.1376344/full

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