The velocity of near-surface materials is one of the most important for Ground-Penetrating Radar (GPR). In the study, we evaluate the options for determining the GPR velocity to measure the accuracy of velocity approximations from the acquired GPR data at an experimental site in Hangzhou, China. A vertical profile of interval velocities can be estimated from each common mid-point (CMP) gather using velocity spectrum analysis. Firstly, GPR data are acquired and analyzed using the popular method of hyperbola fitting which generated surprisingly high subsurface signal velocity estimates while, for the same profile, the Amplitude variation with offset (AVO) analysis of the GPR data (using the same hyperbola fitting method) generate a more reasonable subsurface signal velocity estimate. Several necessary processing steps are applied both for CMP and AVO analysis. Furthermore, experimental analysis is conducted on the same test site to get velocities of samples based on dielectric constant measurement during the drilling process. Synthetic velocities generated by AVO analysis are validated by the experimental velocities which confirmed the suitability of velocity interpretations.

In this research, our focus lies in exploring the effectiveness of a frequency-velocity convolutional neural network (CNN) in the efficient and non-intrusive acquisition of 2D wave velocity visuals of near-surface geological substances, accomplished through the analysis of data from ground-penetrating radar (GPR). To learn complex correlations between antenna readings and subsurface velocities, the proposed CNN model makes use of the spatial features present in the GPR data. By employing a network architecture capable of accurately detecting both local and global patterns within the data, it becomes feasible to efficiently extract valuable velocity information from GPR readings. The CNN model is trained and validated using a substantial dataset consisting of GPR readings along with corresponding ground truth velocity images. Diverse subsurface settings, encompassing different soil types and geological characteristics, are employed to gather the GPR measurements. In the supervised learning approach employed to train the CNN model, the GPR measurements serve as input, while the associated ground truth velocity images are utilized as target outputs. The model is trained using backpropagation and optimized using a suitable loss function to reduce the difference between the predicted velocity images and the actual images. The experimental results demonstrate the effectiveness of the proposed CNN method in accurately deriving 2D velocity images of near-surface materials from GPR antenna observations. Compared to traditional techniques, the CNN model exhibits superior velocity calculation precision and achieves high levels of accuracy. Moreover, when applied to unseen GPR data, the trained model exhibits promising generalization abilities, highlighting its potential for practical subsurface imaging applications.

Ground-penetrating radar (GPR) is one of the most important techniques for obtaining high-resolution data in archaeological research, and it is becoming increasingly important. The continuous wavelet transform (CWT), which is non-numerical technique, gives an overcomplete representation of a signal by continuously varying the wavelet’s translation and scale parameters in the time series dataset. This paper focuses on the novel technique of integrating CWT and the wavelet transform maxima (WTM) to extract information from an archaeological test site in south-eastern China. For the characterization of archaeological features, we assessed the importance of dense and accurate data collection as well as GPR signal processing. The mathematical formulation and applicability of GPR attributes, particularly amplitude-based attributes, to identify and characterize archaeological buried targets are also discussed. GPR data is acquired using co-polarized and cross-polarized configurations with transverse-electric (TE) and transverse-magnetic (TM) broadside frequency plates at 100 and 200 MHz. Next, CWT was applied using six different wavelet levels, followed by amplitude comparison. The archaeological targets were successfully interpreted using peak amplitude and CWT. The proposed methodology has significantly improved data visualization and interpretation of GPR data, and it also gave us good results in identifying archaeological anomalies.

Spectral transform, known as a curvelet, enables sparse representations of complex data. Denoising wave propagation in disordered media and pattern recognition are just a few of the numerous domains in which denoising has a potential application. Based on directional basis functions, this spectral method represents objects with discontinuities along a smooth curve. In this study, we used this technique to eliminate ground roll, an unwanted feature signal that can be seen in seismic data obtained by sonating the earth’s geological formations. Additionally, we improved the curvelet transform threshold denoising method combined with a fast non-local mean to remove seismic random noise. First, cyclic translation and block complex domain threshold methods were introduced into the curvelet transform threshold denoising, and the traditional curvelet threshold denoising method was improved to obtain the best denoising results; Subsequently, the removed noise was filtered using a fast non-local mean method to obtain a valid signal. Finally, the data obtained in the above two steps were added to obtain the final denoising result. The results of the model tests and actual seismic data denoising showed that the denoising results obtained using this method had a higher signal-to-noise ratio and fidelity than that using the other methods.

When magmatic rock intrudes a coal seam, it destroys the coal seam’s structure, reduces the coal quality, and brings hidden dangers to the safe production processes of the coal mine. In this study, the mechanism of magmatic rock intrusion into coal seams was briefly described. In addition, the changes in the structure, composition, and geophysics characteristics of the coal seams following the intrusion of magmatic rock were summarized. This study also performed a forward modeling was performed. Then, the effectiveness of the seismic attributes in detecting the ranges of the coal seams intruded by magmatic rocks was theoretically analyzed. It was observed that after the intrusion of magmatic rocks in the coal seams, the seismic attributes were dramatically affected. For example, the frequency band width values increased, central frequency values decreased, and the main amplitude values decreased. For experimental verification purposes, this study selected the test project of the Yuandian coal mine in the Huaibei area of Anhui, China as an example. The accuracy levels for the detection of the ranges of the coal seams intruded by magmatic rock with seismic attributes were successfully verified.