Semi-Supervised W-KAN for Regularly Missing Seismic Data Reconstruction

Supervised seismic data reconstruction methods are often limited by the quality and quantity of labels in practical applications, and some self-supervised and unsupervised methods suffer from higher computational cost. In addition, most deep models trained with clean datasets possess weak noise robustness. To address the above challenges, we develop a method that makes a trade-off in reconstruction precision, efficiency, demand for labels, and noise robustness. Specifically, we propose a deep model (W-KAN) integrating wavelet and Kolmogorov-Arnold Network (KAN) for regularly missing seismic data reconstruction, which employs discrete wavelet transform (DWT) downsampling to preserve more critical details information and KAN blocks to capture high-level feature representations, which effectively improves the reconstruction precision. We also give a Bernoulli sampling (BS)-based semi-supervised learning strategy that avoids the need for a large number of high-quality labels and enhances noise robustness. We evaluate the proposed method on synthetic as well as field data. Numerical experiments demonstrate the effectiveness and real-time capability of the proposed method, and reveal its competitiveness as well as superiority compared to three deep learning (DL)-based benchmark methods.