Surrounding rock quality of underground caverns is crucial to structural safety and stability in geological engineering. Classic measures for rock quality investigation are destructive and time consuming, and therefore technology evolution for efficiently evaluating rock quality is significantly required. In this paper, the non-destructive technology ground penetrating radar (GPR) assisted by an ensemble empirical mode decomposition (EEMD)-based signal processing approach is investigated for identifying unstable subsurface rock structures. By decomposing the pre-processed GPR signals into multiple intrinsic mode functions (IMFs) and residues, one typical IMF can preserve the distinct local modes and is considered to reconstruct the subterranean profile. Promising results have been achieved in simple scenarios and filed measurements. The reconstructed profiles can accurately illustrate the subsurface interfaces and eliminate the interference signals. Unstable rock structures have been identified in further field applications. Therefore, the developed approach is efficient in unstable rock structure identification.@en

Automatic and efficient ground penetrating radar (GPR) data analysis remains a bottleneck, especially restricting applications in real-time monitoring systems. Deep learning approaches have good practice in automatic object identification, but their intensive data requirement has reduced their applicability. This paper developed a machine learning framework based on wavelet scattering networks to analyze GPR data for subsurface pipeline identification. Wavelet scattering network is functionally equivalent to convolutional neural networks, and its null-parameter property is intended for non-intensive datasets. A double-channel framework is designed with wavelet scattering networks followed by support vector machines to determine the existence of pipelines on vertical and horizontal traces separately. Classification accuracy rates arrive around 98% and 95% for datasets without and with noises, respectively, as well as 97% for considering surface roughness. Pipeline locations and diameters are convenient to determine from the reconstructed profiles of both simulated and practical GPR signals. However, the results of 5 cm pipelines are sensitive to noises. Nonetheless, the developed machine learning approach presents promising applicability in subsurface pipeline identification.@en

The quality of the surrounding rock is crucial to the stability of underground caverns, thereby requiring an effective monitoring technology. Ground-penetrating radar (GPR) can reconstruct the subterranean profile by electromagnetic waves, but two significant issues, called clutter and hyperbola tails, affect the signal quality. We propose an approach to identify fractured rocks using 2D Wavelet transform (WT) and F-K migration. F-K migration can handle the hyperbola using Fourier analysis. WT can mitigate clutter, distinguish signal discontinuity, and provide signals with a good time-frequency resolution for F-K migration. In the simulation, the migration result from horizontal detail coefficients highlight the crack locations and reduce the scattering signals. Noise has been separated by 2D WT. Hyperbola tails are decomposed to vertical and diagonal detail coefficients. Similar promising results have been achieved in the field measurement. Therefore, the proposed approach can process GPR signals for identifying fractured rock areas.@en