Seismic Data Gap Filling Using the Singular Spectrum Based Analysis

Abstract

Data gaps in the geophysical surveys are inevitable due to various unavoidable reasons, like sudden changes in the topography, malfunctioning of instruments etc. Hence, the interpolation of geophysical records is a common practice in geophysical data interpretation. Generally, data gaps increase the rank of data or data trajectory matrix. Therefore, rank reduction procedures help for recovering the data at the gap through an iterative minimization of misfit between original and recovered data. As discussed in the previous chapters, the SVD based Eigen image, SSA and MSSA methods reduce the rank of data or data trajectory matrices to denoise the data. In an iterative approach, these rank reduction procedures could help for recovering the missing data. In addition to the interpolation techniques, SSA method provides the feature of data gap filling through the iterative reconstruction (Kondrashov et al. 2010; Kondrashov and Ghil 2006; Schoellhamer 2001; Rajesh and Tiwari 2015; Rekapalli et al. 2017). The process of time/data series reconstruction using selected Eigen triplets takes the advantage of Eigen properties of the data to fill the data gaps. Therefore, it is a true preservation of Eigen process and is different from the interpolation technique. Thus, the data gaps filled using SSA methods are more reliable compared to averaging, and interpolation techniques.