T-x frequency filtering of high resolution seismic reflection data using singular spectral analysis

We develop here an efficient approach using singular spectral analysis (SSA) for frequency filtering of seismic reflection data in t-x domain. The abrupt change in geophysical records creates ringing artifacts in the Fourier based filtering operations. We use here complete data adaptive basis functions in SSA filtering, which enables the self-similarity of the data in reconstruction of such sudden changes. We first tested the SSA based filtering algorithm on synthetic seismic data and then applied to real seismic reflection data from Singareni coalfields, Andhra Pradesh, India. The individual trace from each channel in the shot gathers is processed and compared with Fourier and multichannel SSA filtered output. Our analysis demonstrates that SSA filtering attenuated the low frequency ground role and high frequency noise embedded in the seismic record in a more efficient way than the other two methods. The coal formations and faults identified in the stack section of filtered data match quite well with the geological information available in the study region. •We present a SSA based algorithm to filter seismic data in t-x domain.•We have compared the results with MSSA and Fourier filtering methods.•The data adaptive basis functions enable to avoid artifacts in SSA filtering.•Fault structures mapped on the SSA filtered seismic stack section agreed with geology.