Investigation of water pipeline leakage is a matter of great significance for water resources management. Among various approaches, ground-penetrating radar (GPR) attracts increasing attention as a fast and noninvasive technique. However, this method suffers from uncertainties in the stage of interpretation because diffractions from the leak region often are too weak to be identified. To address this problem, we have developed the use of diffraction imaging to enhance imaging and characterization of water leakage. Numerical seepage simulation allows us to establish an equivalent computational analog for a known leak condition. After the collection of parallel GPR survey lines, we apply diffraction separation and imaging techniques on 2D and 3D data. Although the actual situation is generally well reproduced by 2D and 3D results, 3D diffraction imaging provides improved mapping of leakage compared with its 2D counterpart. We then apply the method to field data collected at a training base for water pipeline leakage control in Shaoxing, China. The prediction from imaging results corresponds well with the true position and type of leakage. The laboratory and field experiments substantiate the viability of GPR diffraction imaging and illustrate the potential of using GPR to detect small water leaks.

Migration focusing analysis of diffractions is an increasingly important tool for estimating the large-scale subsurface velocity structure from surface-based common-offset ground-penetrating radar (GPR) reflection data. We present a weighting strategy whose aim is to improve the reliability of estimations of the root-mean-square (RMS) velocity obtained using a local semblance focusing measure. In this regard, we increase the resolution of the inferred semblance spectra through a weighting function that varies in accordance with the sensitivity of a diffraction curve to changes in velocity. The weighting function is derived from coherency and slope attributes of the diffracted wavefield components. To demonstrate the viability of our proposed method, we consider its application in two synthetic test cases and one field GPR dataset. Compared with conventional unweighted local semblance spectra, their weighted counterparts allow for a significantly increased resolution and correspondingly reduced picking uncertainty.

Ground deformation associated with rapid urbanization has emerged as a common global challenge facing cities. However, identifying its contributing mechanisms remains difficult because multiple factors, including groundwater management, engineering loads, and extreme weather events, are often superimposed in space and time. This study proposes a scale-aware analytical framework that integrates Small Baseline Subset InSAR (SBAS-InSAR), Stationary Wavelet Transform (SWT), and Independent Component Analysis (ICA) to separate deformation signals by spatial scale and statistical structure, and then support the interpretation of their potential drivers. The original deformation field is first decomposed into a large-scale background component and a small-scale local component using SWT. ICA is then applied to the large-scale background component to extract two statistically separated deformation modes with distinct spatiotemporal characteristics. These modes are further examined using Least-Squares Cross-Wavelet Analysis (LSCWA) and ancillary hydrological information to evaluate their associations with groundwater-related processes. We applied and validated this framework in Zhengzhou, China, as a representative case. Results show that Zhengzhou exhibits a clear “central uplift and peripheral subsidence” pattern. The large-scale component contains two statistically separated modes. The first mode shows a long-term uplift pattern that is spatially consistent with the groundwater no-take zone and temporally associated with confined-aquifer water-level recovery. The second mode shows an annual oscillation that is coherent with groundwater storage anomaly, with an estimated deformation lag of approximately 1–2 months. The small-scale local component highlights subsidence bowls that are spatially and temporally associated with engineering and land-use features, including newly constructed transportation corridors, high-density building areas, landfills, and waterfront park projects. The proposed framework provides a scale-aware pathway for interpreting complex urban deformation fields and may support more targeted geohazard monitoring and urban planning in areas affected by coupled hydrological and anthropogenic pressures.

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting.