Adaptive Graded Denoising of Seismic Data Based on Noise Estimation and Local Similarity

TL;DR

This paper introduces a novel adaptive seismic data denoising method that estimates noise levels and uses local similarity to selectively re-denoise regions, effectively handling uneven noise.

Contribution

It proposes a new graded denoising approach combining noise estimation, local similarity, and iterative re-denoising to improve seismic data quality.

Findings

Effective on theoretical models and real seismic data

Handles uneven noise well

Reduces over- and under-denoising issues

Abstract

Seismic data denoising is an important part of seismic data processing, which directly relate to the follow-up processing of seismic data. In terms of this issue, many authors proposed many methods based on rank reduction, sparse transformation, domain transformation, and deep learning. However, when the seismic data is noisy, complex and uneven, these methods often lead to over-denoising or under-denoising. To solve this problems, we proposed a novel method called noise level estimation and similarity segmentation for graded denoising. Specifically, we first assessed the average noise level of the entire seismic data and denoised it using block matching and three-dimensional filtering (BM3D) methods. Then, the denoised data is contrasted with the residual using local similarity, pinpointing regions where noise levels deviate significantly from the average. The remaining data is retained intact. These areas are then re-evaluated and denoised. Finally, we integrated the data retained after the first denoising with the re-denoising data to get a complete and cleaner data. This method is verified on theoretical model and actual seismic data. The experimental results show that this method has a good effect on seismic data with uneven noise.