In this paper we discuss high-resolution coherence functions for the estimation of stacking shape parameters in seismic signal processing. We focus on the MUltiple SIgnal Classification (MUSIC) algorithm, which uses the eigendecomposition of the seismic data to measure the coherence. MUSIC can outperform the traditional semblance in cases of close or interfering reflections. Our main contribution is to propose several simplifications to the implementation of MUSIC. Namely, we propose an iterative way to obtain the MUSIC coherence function, called Power Method MUSIC (PM-MUSIC). We also propose a new way to obtain the MUSIC pseudospectrum, based on the eigendecomposition of the temporal covariance matrix of the seismic data. This is in contrast to the algorithms in the literature, which are based on the spatial covariance. We compared spatial and temporal covariance matrices, implemented with PM-MUSIC, in a simple synthetic example with two reflections corrupted by additive white gaussian noise. Initial simulations indicated that PM-MUSIC outperforms semblance and that the temporal variant of PM-MUSIC is superior to its spatial counterpart. Moreover, temporal PM-MUSIC is particularly useful when dealing with correlated signals.


Article metrics loading...

Loading full text...

Full text 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