Resolution enhancement of deconvolved ground penetrating radar images using singular value decomposition

Abstract

Abstract Sharpening ground-penetrating radar (GPR) images remains a persistent challenge. Two approaches in particular—singular value decomposition (SVD) and sparse blind deconvolution (SBD)—have been shown to effectively denoise GPR images and resolve a reflectivity model, respectively. To our knowledge, these approaches have not been used to process GPR data from archaeological sites. This paper highlights the treatment of a sample GPR profile acquired over two graves in sandy soil. SVD can remove user-specified components of the GPR image such as the direct wave, horizontal banding, and ringing from surface features. These are removed via a low order eigenimage filter in which the decomposed GPR data is reconstructing without the eigenimages containing them. This type of filter significantly alters the frequency content and total amplitude, which may or may not negatively influence subsequent filtering techniques or data analysis. Thus, we use SBD to investigate the influence of eigenimage filtering. The eigenimage-filtered GPR image returns a more physically meaningful reflectivity model compared to SBD of the original GPR profile. We additionally test the common approach ‘denoising’ GPR data by also omitting higher-order eigenimages from the reconstructed GPR image. The resulting reflectivity model was less sparse indicating that higher-order eigenimages contain signal information critical to deconvolution. To test the effectiveness of other filtering steps with SVD filtering, we compare the migration of the original GPR profile against that of an eigenimage-filtered image. The migrated filtered image, particularly if higher-order eigenimages are preserved, provides more spatially constrained point reflectors regardless of the migration algorithms used compared to the migrated original profile.