1887

Abstract

Summary

The application of Elastic Full Waveform Inversion (EFWI) in large-scale 3D imaging projects has emerged as a significant advancement in seismic data processing. Given the substantial computational resources required for these methods, there are high expectations for the quality of the results. To enhance preprocessing efficiency and ensure superior outcomes, we investigate the impact of a deep learning denoising algorithm within the EFWI workflow.

Convolutional neural networks (CNNs) and generative adversarial networks (GANs) have been employed to develop robust models capable of several tasks, including image denoising. These deep learning models leverage large datasets, including synthetic and real seismic data, to infer the underlying patterns of noise and implement effective denoising strategies. Notably, the deep learning algorithm we utilize allows for a computationally efficient inference stage that requires minimal parametrization, thereby reducing the turnaround time for processing projects.

Despite wide-spread claims that FWI can be applied to raw data with no preprocessing, our findings reveal that more robust denoising aids in the evaluation of time-shift objective functions for FWI. This can improve convergence, achieving a higher-resolution velocity model, when considering execution for the same frequency bands. We present results for a deep-water field 3D Ocean Bottom Node (OBN) seismic acquisition.

© EAGE Publications BV
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/content/papers/10.3997/2214-4609.202510610
2025-06-02
2026-02-06

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References

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/content/papers/10.3997/2214-4609.202510610
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The Impact of Deep Learning Seismic Denoising in 3D Elastic FWI of OBN Data
Conference Proceedings 2025, 1 (2025); https://doi.org/10.3997/2214-4609.202510610
/content/papers/10.3997/2214-4609.202510610
/content/papers/10.3997/2214-4609.202510610
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