Efficient Seismic Random Noise Attenuation via KAN-Empowered Neural Low-Rank Representation

Seismic data inevitably suffers from random noise due to environmental contributors, which seriously affects subsequent processing and analysis. Deep learning has been a successful tool for seismic data random noise attenuation. Due to the scarcity of clean labels in real scenarios, researchers have attached more attention to self-supervised methods without paired training data. However, most self-supervised methods are costly in computations and thus are inefficient for practical large data volume implementation. In this article, we propose a novel self-supervised method for seismic random noise attenuation by designing a Kolmogorov-Arnold network (KAN)-empowered neural low-rank representation (NLRR) method. Specifically, the proposed method adopts a compact tensor factorization parameterized by implicit neural representations (INRs) to efficiently encode both low-rank and smooth priors of seismic data into the model. Moreover, we introduce generalized KANs by using multiple sinusoidal activation functions with different frequencies, serving as factor functions of NLRR to empower its representation ability. Extensive experiments on synthetic and field seismic data demonstrate the clear superiority of our method in terms of efficiency and efficacy over several traditional and deep learning-based methods for random noise attenuation. Specifically, our method reduces over 90% execution time against existing self-supervised methods while still achieving evidently better denoising results.