Fourier-based algorithms originally developed for the processing of seismic data are applied routinely in the Ground-penetrating radar (GPR) data processing, but these conventional methods of data processing may result in an abundance of spurious harmonics without any geological meaning. We propose a new approach in this study based essentially on multiresolution wavelet analysis (MRA) for GPR noise suppression. The 2D GPR section is similar to an image in all aspects if we consider each data point of the GPR section to be an image pixel in general. This technique is an image analysis with sub-image decomposition. We start from the basic image decomposition procedure using conventional MRA approach and establish the filter bank accordingly. With reasonable knowledge of data and noise and the basic assumption of the target, it is possible to determine the components with high S/N ratio and eliminate noisy components. The MRA procedure is performed further for the components containing both signal and noise. We treated the selected component as an original image and applied the MRA procedure again to that single component with a mother wavelet of higher resolution. This recursive procedure with finer input allows us to extract features or noise events from GPR data more effectively than conventional process.To assess the performance of the MRA filtering method, we first test this method on a simple synthetic model and then on experimental data acquired from a control site using 400. MHz GPR system. A comparison of results from our method and from conventional filtering techniques demonstrates the effectiveness of the sub-image MRA method, particularly in removing ringing noise and scattering events. Field study was carried out in a trenched fault zone where a faulting structure was present at shallow depths ready for understanding the feasibility of improving the data S/N ratio by applying the sub-image multiresolution analysis. In contrast to the conventional methods, the MRA sub-image filtering technique provides an overall improvement in image quality of the data as shown in the field study.
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