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Improvement of FWI with Progressive Transfer Learning
- Publisher: European Association of Geoscientists & Engineers
- Source: Conference Proceedings, Asia Petroleum Geoscience Conference and Exhibition (APGCE), Nov 2022, Volume 2022, p.1 - 5
Abstract
Summary
The lack of low-frequency data components has been a major obstacle in FWI applications for velocity model building. Many theoretical approaches have been proposed to extrapolate low-frequency components. Progressive transfer learning was proposed to solve the problem by using a deep learning-based approach to predict low-frequency components. In this paper, we demonstrate the effectiveness of the progressive transfer learning workflow by building a practical workflow and applying it to the field data.
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