THE EFFECT OF SIGNAL-TO-NOISE RATIO ON GROUND ROLL ATTENUATION USING ADAPTIVE SINGULAR VALUE DECOMPOSITION: A CASE STUDY FROM THE SOUTH WEST OF IRAN

摘要

Ground roll as a type of coherent noise with high amplitude, low frequency and low velocity masks the reflections in seismic data. Ground roll suppression is one of the important topics in data processing. Adaptive Singular Value Decomposition (ASVD) is a coherency based filter which decomposes data into its eigenimages and can detect horizontal events in the first eigenimages. By using the adaptive method, the ground roll is converted to a horizontal event. By zeroing the first eigenvalues which present ground roll, it can be suppressed. By increasing the number of the eigenvalues to be zeroed, ground roll is better attenuated but more reflections are damaged as well. In this study, this filter is applied to synthetic data with various signal-to-noise ratios (SNR) and two real shot records from the South West of Iran as case studies. The first one was examined with high and low SNRs (with adding the noise). However, in the second one, extensive presence of ground roll and other noises led to an extreme decrease in SNR. Results of applying the filter to the synthetic and field data sets with various SNRs showed that the ASVD filter could attenuate the ground roll with minimum harm to signals and it was not sensitive to SNR, because the eigenvalues were sorted in a descending order in the eigenvalue spectrum. Therefore, after rotating the data, the ground roll as a horizontal and coherent event, was represented in the first eigenvalues. However, the random noise or reflections had lower energies and coherencies compared to the flattened ground roll and they were represented in the next eigenvalues. Due to this separation, the SNR has no impact on the ground roll attenuation via ASVD.