Seismic data denoising using the 3D curvelet transform method

Abstract

One of the major difficulties in processing and interpreting seismic data is the contamination of seismic signals by noise from numerous sources. Conventional denoising methods are mostly used for processing 2D seismic data, but noise also exists in the 3D data space, resulting in poor results using conventional denoising methods. Consequently, here, for seismic data denoising, a multiscale and multidirectional 3D curvelet transform was adopted. Our study combined the core principle of the 3D curvelet approach with the threshold iteration method to denoise simulated and actual seismic data with varying signal-to-noise ratios, and the results were quantitatively compared with the processing outcomes of existing denoising methods. The effectiveness of our method is illustrated using both genuine 2D and 3D post-stack seismic data, and synthetic 2D sections with added white and colored noise. Finally, we demonstrate how to prepare the data for frequency-domain full-waveform inversion using curvelet denoising. Despite the complexity of the procedure used to create the training samples, testing results using synthetic and real seismic data demonstrate that this method has mastered the capacity to suppress Gaussian and super-Gaussian noise from various training samples.

Keywords:

• Curvelet transform threshold
• 3D denoising
• frequency domain
• high signal-to-noise ratio
• noise suppression
• seismic data transformation

Acknowledgements

We gratefully acknowledge the Editors and Reviewers for providing thoughtful and useful suggestions.

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research is supported by the National Natural Science Foundation of China (No. 42062015), the Emergency Management Joint Innovation Technology Project of Guangxi (2024GXYJ034), and the Science and Technology Base and Talent Project of Guangxi (AD23026015).