Reviving Legacy Seismic Data via Machine Learning Technique-Part 2: Estimating 3-D Seismic Volumes From 2-D Seismic Lines With VQ-VAE

We propose a machine learning-based method that estimates a 3-D seismic volume from irregularly placed 2-D seismic lines, addressing the challenges regarding the local disturbances contained within 2-D lines (e.g., seismic misties and discrepant seismic characteristics across lines). To overcome the...

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Bibliographic Details
Published in:	IEEE transactions on geoscience and remote sensing Vol. 63; pp. 1 - 21
Main Authors:	Lee, Jun-Woo, Je Lee, Min, Min, Dong-Joo, Cho, Yongchae
Format:	Journal Article
Language:	English
Published:	New York IEEE 2025 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:	2-D line interpolation 2-D/3-D seismic data 3-D volume data augmentation 3-D volume estimation Convolution D lines Data augmentation Disturbances Estimation Filters Geological structures Geology Interpolation Intervals Learning algorithms Machine learning Neural networks Random signals Seismic data Surveys Three-dimensional displays Training Two-dimensional displays vector-quantized variational autoencoder (VQ-VAE) Volume measurement
ISSN:	0196-2892, 1558-0644
Online Access:	Get full text
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