Disentangling Noise Patterns From Seismic Images: Noise Reduction and Style Transfer

Seismic interpretation is a fundamental approach for obtaining static and dynamic information about subsurface reservoirs, such as geological faults/salt bodies and associated fluid types and distribution. Due to the exponential growth in seismic data volume and considerable uncertainty in manual interpretation, deep learning (DL) algorithms have been introduced to assist seismic interpretation. Our investigation of the trained neural networks suggests that they underperform on seismic data with different noise characteristics. One of the main issues is that the noise patterns of seismic data are highly inconsistent due to many factors, including geological features, sampling parameters, and human intervention. To address this problem, we propose a noise pattern transfer (NPT) framework to transfer or remove seismic noise style between datasets by treating noise patterns as styles of image, which can also improve the generality of automatic seismic interpretation algorithms. Extensive experiments on three synthetic datasets and two field seismic datasets demonstrate the promising performance of our proposed NPT approach. Pairs of clean and stylized seismic data are generated by extending the use of the neural style transfer algorithm beyond the artistic domain. We then demonstrate how our method achieves superior noise pattern transferability between datasets and denoising performance on field datasets. Associated improvements in accuracy and generalization of the neural-network-based fault recognition tasks successfully demonstrate the practicality of our NPT approach. The source code is made publicly available online at https://github.com/Magnomic/npt-code .