Hybrid loss and domain transform based seismic image blind denoising

Abstract

Noise removal is a critical stage in the processing of seismic data. Recent data-driven approaches require pre-assumptions about the distribution of noise for training the model, but in the actual denoising work, the natural noise does not obey the assumed distribution and the ability of the network to deal with this kind of noise is limited. Therefore, this paper proposes an adversarial learning method, FDT, which based on hybrid loss and domain transform. It treats noisy seismic data and clean seismic data as two different domains, and performs adversarial training by cycling between the two domains. Then the network will automatically learn a mapping that converts noisy seismic data into clean seismic data, which can achieve promising denoising result despite the noisy distribution is unknown. Moreover, single pixel-level loss similar to L1 and L2 is not adequate to characterize the texture and structure of the seismic data, in this paper, we combined SSIM(Structure Similarity Index Measure) and L1 together as a reconstruction loss to ensure that seismic data remains the same geological and tectonic information after denoising. Experimental results demonstrate that the method in this paper can effectively remove most types of noise from seismic data and achieve advanced results on both synthetic and real datasets. And it outputs a PSNR(Peak Signal-to-Noise Ratio) value of 27.97 with a standard deviation of 0.2 for large scale noise, maintaining a highly reliable denoising performance.