1887
PDF

Abstract

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.

Loading

Article metrics loading...

/content/papers/10.3997/2214-4609.201403009
2012-05-14
2024-12-06
Loading full text...

Full text loading...

/content/papers/10.3997/2214-4609.201403009
Loading
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error