Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the com-plexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accur...
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| Format: | Article |
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MDPI
2022
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| Online Access: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133677820&doi=10.3390%2fapp12136723&partnerID=40&md5=900b9ff5da770a934397bf14cdf6bfc5 http://eprints.utp.edu.my/33360/ |
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