A STUDY ON ALGORITHM FOR RECONSTRUCTION OF DE-ALIAS UNEVEN SEISMIC DATA
反假频非均匀地震数据重建方法研究

Spacial trace interpolation is one of the most important issues in seismic data processing. In this paper, a novel Fourier transform based algorithm is proposed, which can reconstruct both uneven and alias seismic data. We formulate band-limited data reconstruction as a minimum norm least squares (LS) type problem where an adaptive DFT-weighted norm regularization term is used. The inverse problem is solved by the pre-conditional conjugate gradient method, which makes the solutions stable and convergence quick. Based on the assumption that seismic data are consisted of finite linear events, and from sampling theorem, alias events can be attenuated via LS weight predicted linearly from low frequency. Three application issues are discussed on even gap trace interpolation, uneven gap filling, and high frequency trace reconstruction from low frequency data trace constrained by few high frequency traces. Both synthetic and real data numerical examples show the proposed method is valid, efficient and applicable.