Article,
Two-dimensional coherent noise suppression in seismic data using eigendecomposition
IEEE Transactions on Geoscience and Remote Sensing, 29 (3): 379--384 (May 6, 1991)
DOI: 10.1109/36.79428
Abstract
A method for the suppression of coherent noise in seismic data based
on the eigendecomposition of a data covariance matrix is demonstrated.
Based on the Karhunen-Loeve transform, the proposed procedure is
useful against noise energy exhibiting both two-dimensional space
and time coherencies or coherent two-dimensional patterns which are
not necessarily linear and therefore cannot generally be velocity-filtered.
This method trains on a region containing the undesired coherent
noise; the dominant eigenvectors determined from the covariance matrix
of that noise are used to reconstruct the noise in the region of
interest. Subtracting the reconstruction from the original data leaves
a residual in which the coherent noise has been suppressed. In the
example considered, this method effectively suppresses the noise
in a record of marine seismic data containing backscattered source
energy.
