Sparse blind deconvolution of common-offset GPR data

Jazayeri, Sajad (University of South Florida) | Ebrahimi, Alaeddin (Sultan Qaboos University) | Kruse, Sarah (Sultan Qaboos University)


Ground penetrating radar (GPR) is widely used for shallow (cms to tens of meters) subsurface imaging. Full-waveform inversion (FWI) of GPR data has enabled researchers to increase the subsurface image resolution. The FWI technique has been applied primarily to off-ground GPR and crosshole GPR data, due to complexity of surface-based on-ground data. A major difficulty with common-offset surface GPR data is the source wavelet estimation, particularly due to presence of the air wave, ground wave and noise. Existing deconvolution methods for estimation of the source wavelet require preliminary knowledge of the subsurface. Sparse blind deconvolution (SBD) methods permit estimation of the source wavelet without an initial synthetic model of soil structure. We investigate the performance of an SBD method to estimate the source wavelet of common-offset GPR data The effects of including/excluding the air/ground wave are studied on both synthetic and simple field test data involving a single buried pipe. For the 2D synthetic model, SBD extracts the wavelets. For the real field data, the estimated wavelets are compared to those derived from the deconvolution method. Ongoing research will examine the relative quality of FWI inversion results based on wavelets estimated with SBD..

Presentation Date: Monday, September 25, 2017

Start Time: 4:20 PM

Location: 360C

Presentation Type: ORAL