Abstract - 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 in-
terpretation, 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 culprits is
that the noise pattern of seismic data is 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 stylised 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 generalisation 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.
IEEE Transactions on Geoscience and Remote Sensing, 60, https://doi.org/10.1109/TGRS.2022.3219117, 2022.