Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

Improving Performance of Seismic Fault Detection by Fine-Tuning the Convolutional Neural Network Pre-Trained with Synthetic Samples

Fault interpretation is an important part of seismic structural interpretation and reservoir characterization. In the conventional approach, faults are detected as reflection discontinuity or abruption and are manually tracked in post-stack seismic data, which is time-consuming. In order to improve...

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Bibliographic Details
Main Authors: Zhe Yan, Zheng Zhang, Shaoyong Liu
Format: Article
Language:English
Published: MDPI AG 2021-06-01
Series:Energies
Subjects:
fault detection
deep learning
U-net
transfer learning
Online Access:https://www.mdpi.com/1996-1073/14/12/3650

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https://www.mdpi.com/1996-1073/14/12/3650

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