Denoising Laplace-domain Seismic Wavefields using Deep Learning

Random noise in seismic data can significantly impair hydrocarbon exploration by degrading the quality of subsurface imaging. We propose a deep learning approach to attenuate random noise in Laplace-domain seismic wavefields. Our method employs a modified U-Net architecture, trained on diverse synthetic P-wave velocity models simulating the Gulf of Mexico subsurface. We rigorously evaluated the network’s denoising performance using both the synthetic Pluto velocity model and real Gulf of Mexico field data. We assessed the effectiveness of our approach through Laplace-domain full waveform inversion. The results consistently show that our UNet approach outperforms traditional singular value decomposition methods in noise attenuation across various scenarios. Numerical examples demonstrate that our method effectively attenuates random noise and significantly enhances the accuracy of subsequent seismic imaging processes.