Abstract
Ground Penetration Radar is a controlled source geophysical method which uses high frequency electromagnetic waves to study shallow layers. Resolution of this method depends on difference of electrical properties between target and surrounding electrical medium, target geometry and used bandwidth. The wavelet transform is used extensively in signal analysis and noise attenuation. In addition, wavelet domain allows local precise descriptions of signal behavior. The Fourier coefficient represents a component for all time and therefore local events must be described by the phase characteristic which can be abolished or strengthened over a large period of time. Finally basis of Auto Regression (AR) is the fitting of an appropriate model on data, which in practice results in more information from data process. Estimation of the parameters of the regression model (AR) is very important. In order to obtain a higher-resolution spectral estimation than other models, recursive operator is a suitable tool. Generally, it is much easier to work with an Auto Regression model. Results shows that the TQWT in soft thresholding mode can attenuate random noise far better than TQWT in hard thresholding mode and Autoregressive-FX method.
Highlights
IntroductionThe use of this method began in 1956 and has been developed since the 1970s
Results shows that the Tunable Quality Wavelet Transform (TQWT) in soft thresholding mode can attenuate random noise far better than TQWT in hard thresholding mode and Autoregressive-FX method
In this paper synthetic and real GPR data was denoised with the TQWT in two soft and hard thresholding and the Autoregressive-FX filter
Summary
The use of this method began in 1956 and has been developed since the 1970s. Resolution of GPR varies from depth of centimeters to a several meters with maximum depth of about 100 meters Resolution of this method depends on difference of electrical properties between target and surrounding electrical medium, target geometry and used bandwidth. The wavelet Transform may allow separation of signal components which overlaps in time and frequency. It is much easier to work with an Auto Regression model For this reason, suitable techniques are presented for estimating of model parameters [5]
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have