1887

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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/content/papers/10.3997/2214-4609.202270025
2022-11-28
2024-04-28
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References

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