Sparsity based GPR blind deconvolution and wavelet estimation 7 Sparsity based GPR blind deconvolution and wavelet estimation
Improving the vertical resolution of Ground Penetrating Radar (GPR) data by blind deconvolution technique is an approach we target here. Sometimes geologic situations such as presence of clay or humidity lead to blurred sections. Advanced processing steps, that are so common in seismic reflection, such as deconvolution are needed. In this approach the sparse deconvolution algorithm on GPR data has been used in a novel way. It is often assumed that reflectivity series are sparse and noise is random. Generalized Cross Validation (GCV) method has been used to estimate the desired wavelet and to find the optimum iteration in the deconvolution algorithm. To examine the efficacy of the method, it is applied to synthetic data. The GCV and MSE curves versus iteration are then plotted in order to determine the optimal point. The final deconvolved section shows a satisfying result for GPR field data and the speed and accuracy of this robust algorithm to reconstruct reflectivity series is considerable.