Denoising of pre‐stack seismic data using subspace estimation methods. Issue 8 (1st October 2018)

• Record Type: Journal Article
• Authors: Elumalai, Karthikeyan; Kumar, Shailesh; Lall, Brejesh; Patney, Rakesh Kumar
• Is Part Of: IET signal processing. Volume 12:Issue 8(2018)
• Page Start: 992
• Page End: 999
• Publication Date: 2018-10-01
• Subjects: seismology -- geophysical signal processing -- iterative methods -- signal representation -- signal denoising; pre‐stack seismic data denoising -- seismic data processing flow -- subspace estimation methods -- receivers -- earth subsurface layers -- common mid‐point -- CMP -- time delay -- geophones -- low‐dimensional subspace -- ambient signal space -- sparse representations -- orthogonal matching pursuit -- field seismic data -- random noise; Signal processing -- Periodicals; 621.3822
• DOI: 10.1049/iet-spr.2017.0556
• Languages: English
• ISSNs: 1751-9675

Abstract

Abstract : Denoising is one of the core steps in seismic data processing flow. The seismic gather consists of multiple traces captured at different receivers. A set of receivers observe waves which are reflected from the same reflection point. Those traces need to be grouped together as they contain the same information about the earth subsurface layers. This is done by finding a common mid‐point (CMP) between the source and geophones. The time delay between CMP gathered traces are corrected by the normal move out correction method but the individual traces are corrupted by noise. In this paper we, propose a method for denoising individual traces. The set of traces can be modelled as belonging to a low‐dimensional subspace of an ambient signal space. This allows for construction of sparse representations of each trace in terms of other traces in the CMP gather. The resulting sparse representations are subsequently utilised to construct approximations of individual traces and thus, noise is suppressed. We constructed, the approximations using orthogonal matching pursuit. We applied proposed method to synthetic and field seismic data, the proposed technique performs better on established benchmarks while capturing the true locations of weak reflections and effectively attenuating the random noise.