An integrated m ethod of seism ic data reconstruction and denoising based on generative adversarial network
During the actual acquisition process,due to terrain conditions and human factors,seismic data can suffer from spatial under sampling or irregular sampling,as well as being contaminated by random noise,which hinders subsequent processing and interpretation.Current seismic data processing methods typically separate re-construction and denoising into two stages,often introducing additional errors.The focus of the integrated re-construction and denoising method is to accurately extract the effective features of seismic data under mixed in-terference from missing traces and noise.This paper proposes an integrated method for seismic data reconstruc-tion and denoising based on conditional Wasserstein generative adversarial network(cWGAN).Firstly,a ge-nerator model is constructed with the U-Net model as the basic network structure,and the event features of seis-mic data are extracted.Conditional constraints are then introduced into the discriminator model to guide the gra-dient optimization direction of the generator.Secondly,an error description model for reconstruction and de-noising is established,and an integrated loss function is designed to address both tasks simultaneously.Finally,tests on synthetic and actual data demonstrate that the seismic data recovered by the proposed network model have a higher signal-to-noise ratio and good robustness.
seismic data processingintegrated method of reconstruction and denoisingdeep learning,generative adversarial networkintegrated loss function
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