oa Подавление шума многокомпонентных сейсмических данных с использованием FX домена SVD

ral covariance matrix of seismic wavefield data in F‐x domain is formed and in order to avoid dealing with very large matrixes, the reduced dimensional spectral covariance matrix is estimated by means of singular value decomposition (SVD). By finding the highest eigenvalue of the reduced dimensional covariance matrix we are able to separate the desired seismic waves from the noise. The results have shown that the proposed algorithm outperforms the conventional separation technique in terms of accuracy and complexity.