Singular value decomposition is an effective way to remove ground clutter in ground-penetrating radar (GPR) applications. The main limitation of this method is the selection of principal components to completely reconstruct the ground clutter or the target. To date, no effective criteria or technology have been developed. To solve this problem, a new method is proposed in this paper. The research and analysis presented herein reveal that the root-mean-square height (RMSH) of the first-arrival curve corresponding to the ground clutter has a well-defined positive relationship with the number of singular values associated with the principal components of the ground clutter. The number of singular values of these principal components (N) can be precisely determined based on the ground clutter by a linear function, N = 0.2634D + 1.3086, where D represents the RMSH value. In addition, an algorithm called developed histogram equalization was developed to improve the contrast to highlight the targets in denoized GPR data sets. The proposed strategy of extracting the principal components of the ground clutter and highlighting the contrast between the target signal and environmental reflections was successfully applied to the field GPR data, thus demonstrating the practicality and validity of the proposed approach.

Caianiello neural networks (CNNs) incorporated with the Robinson seismic convolutional model are modified by the Levenberg-Marquardt algorithm to improve convergence. CNNs are extended to the multiattribute domain for reservoir property inversion, with time-varying signal processing by a frequency-domain block implementation using fast Fourier transforms. Optimal inversion can be achieved by applying the Levenberg-Marquardt optimization to multiattribute domain CNNs for convergency improvement due to its ability to swing between the steepest-descent and Gauss-Newton algorithms. The methodology is applied to porosity estimation in an oilfield with six wells in the Bohai Basin of China. Cross-validation results indicate significant correlation between actual porosity logs and predicted porosity logs. Compared with a traditional method, our technique is robust.