Abstract
The seismic data are always spatially undersampled during filed acquisition, limited by the natural environment and economic conditions. Irregularly missing seismic traces can affect the subsequent processing of seismic data, ultimately impacting the imaging and inversion results. Therefore, seismic data regularisation and interpolation are particularly necessary and have become an important step for data pre-processing. This abstract presents an improved GC algorithm with partial convolution layers and spectral normalization and applies it on 2D seismic data reconstruction. Compared with the traditional cGAN reconstruction method, The proposed method can effectively enhance the generative capability of the cGAN algorithm. Both synthetic and field data experiments demonstrate that the reconstruction performance of the improved cGAN method is better than the conventional cGAN method.
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