Top-coal structure detection is an important basis for realizing effective mining in fully mechanized cave faces. However, the top-coal structure is very complex and often contains multi-layer gangues, which seriously influence the level of effective mining. For these reasons, this paper proposes a novel multi-frequency ground-penetrating radar (GPR) data-fusing method through a joint sliding window and wavelet transform weighting method to accurately detect the top-coal structure. It possesses the advantages of both high resolution and great detection depth, and it can also integrate multi-frequency GPR data into one composite profile to interpret the internal structure information of top coal in detail. The detection procedure is implemented following several steps: First of all, the multi-frequency GPR data are preprocessed and aligned through a band-pass filter and a zero offset elimination method to establish their spatial correspondences. Secondly, the proposed method is used to determine the time-varying weight values of each frequency GPR signal according to the wavelet energy proportion within the sliding window; also, the edge detection algorithm is introduced to improve the fusion efficiency of the wavelet transform so as to realize the effective fusion of the multi-frequency GPR data. Thirdly, a reflection intensity model of multi-frequency GPR signals traveling in the top-coal is established by using the stratified identification method, and then, the detailed top-coal structure can be inversely interpreted. Finally, the quantitative evaluation criteria, information entropy (IE), space–frequency (SF) and Laplacian gradient (LG), are used to evaluate the multi-frequency GPR data fusion’s effectiveness in laboratory and field environments. The experimental results show that, compared with the genetic, time-varying and wavelet transform fusion method, the fusion performance of the presented method possesses higher values in the IE, SF and LG evaluation criteria, and it also has both the merits of high resolution and great detection depth. 

Multi-Frequency GPR Data Fusion through a Joint Sliding Window and Wavelet Transform-Weighting Method for Top-Coal Structure Detection

Traditional cross-hole radar tomography (CRT) usually cannot obtain high-resolution imaging results due to the nonlinearity and multisolution of inversion. To cope with these challenges, we propose a scheme to achieve high-resolution CRT for complex slowness models using deep neural networks (DNNs). Given the inherent difficulty in generating complex geophysical models in batches, by series of processing some remote sensing images from the remote sensing scene classification dataset, we create a real slowness model dataset. Then, we utilize 2-D U-Net to directly construct the mapping relationship between the low-resolution slowness model from the traditional method and the real slowness model. The superiority of our scheme is verified by both synthetic data and measured data. Our scheme can significantly suppress the false anomaly of traditional CRT results and accurately reconstruct the underground target’s geometry, position, and slowness value, and it has excellent accuracy and robustness. In addition, the response data of the slowness model reconstructed by our scheme are closer to the field data.

Slowness High-resolution Tomography of Cross-hole Radar Based on Deep Learning

Comparison of the pyGIMLi and BERT packages for landslide and cavity detection: A case study from Tghat-Oued Fez, Morocco

Cross-borehole ground-penetrating radar (GPR) is one of the most effective tools available to detect underground cavities. These cavities are often detected from zero-offset profiling (ZOP) measurements, where the transmitter and the receiver are always at the same depth; the other common alternative is to use multiple-offset gathers (MOGs), such that a measurement is recorded with the transmitter and the receiver at every pair of depths. While the latter strategy sometimes allows one to compute a tomographic inversion and, in general, gives strictly more information, it is often prohibitively expensive to carry out such a survey, and the utility of many of these measurements is limited. ZOP measurements, on the other hand, are comparatively cheap to carry out, yet, it may not be possible to use these measurements alone to detect cavities with the required degree of accuracy. In this article, the use of measurements with a small offset in depth between the receiver and the transmitter is investigated, and it is shown that the use of only these measurements is a very favorable point in the expense-utility tradeoff. Along the way, it is shown that the manner in which traveltime is measured must be appropriate for the noise regime in which the GPR operates.

Cavity Detection Using GPR With Small Offsets

This paper introduces an alternative approach for detecting cavities in reinforced concrete walls using Ground Penetrating Radar (GPR) A-scan data. GPR, leveraging electromagnetic waves, is extensively applied for cavity detection within structures. The nature of electromagnetic waves, significantly influenced by reflective media and attenuating through them, requires specialized analysis methods for data interpretation. Traditional methods often involve identifying and eliminating overlapping reflection patterns or adjusting signal magnitude at specific depths to isolate peak signals from the target object's surface, which can be subjective and complex. To overcome these challenges, this study proposes quantitatively assessing the presence of cavities by analyzing the integral area of A-scan data within suspected ranges. Observations indicate a substantial difference in reflection patterns between areas with and without cavities, showcasing the potential of this approach for quantitative cavity detection. This approach offers a more objective and quantitative basis for identifying cavities in reinforced concrete structures. 

An Alternative Approach for Detecting Cavities in Reinforced Concrete Structures using GPR A-scan Data

We present a novel method to detect underground cavities using cross-borehole ground-penetrating radar (GPR). We model the propagation of a GPR signal across a cavity and find that the spectrum of the signal will be distorted in a specific low-pass manner. Comparing the spectra of received signals with the predicted spectrum using a hypothesis-testing approach allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Our approach generally remains effective even when conventional methods fail; our method can also be combined with conventional methods in order to more successfully distinguish between cavities and geologic clutter.

https://figshare.com/ndownloader/files/34372991

Underground Cavity Detection Through Spectral Distortion of a GPR Signal

We present a novel method for underground cavity detection using cross-borehole ground-penetrating radar (GPR). Modeling the soil-cavity system as a filter allows us to estimate the group dispersion of the GPR signal induced by propagation through a cavity. We adopt a hypothesis-testing approach based on the observed group dispersion and propose a method which allows us to detect cavities with high probability. The proposed method is validated on both simulated and real measurements. Geologic clutter that causes conventional methods to fail has negligible impact on the efficacy of the proposed method, and the proposed method may be combined with conventional methods to provide a more robust and effective cavity-detection solution.

/pdf/underground-cavity-detection-through-group-dispersion-of-a-hjfvyzdg.pdf

Underground Cavity Detection through Group Dispersion of a GPR Signal

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Underground Cavity Detection Through Spectral Distortion of a GPR Signal

Cavity Detection Using GPR With Small Offsets

Underground Cavity Detection through Group Dispersion of a GPR Signal