It is important to distinguish between two common defects, fatigue cracks and reflective cracks, and determine their locations (the top and bottom) in the highway pavement because they require individually targeted treatment measures. Ground Penetrating Radar (GPR) has the potential to detect cracks in the highway pavement due to the change of the electromagnetic properties of highway-pavement media, arising from the existences of cracks. By using a theoretical analysis and a numerical simulation, we compare the characteristics of corresponding radargrams, including the amplitude variation of diffracted waves with various models of presetting cracks inside the layered homogeneous media. For those fatigue cracks and reflective cracks extending to the road surface, the amplitude curves of direct ground wave can intuitively indicate the locations of the top of the cracks and qualitatively compare the width of these cracks. Furthermore, we find that the shape and pattern of diffraction hyperbolas of both types of cracks with bottoms at different locations are quite similar, but their amplitudes are significantly different. To be specific, for those cracks with the same width, the amplitude of diffracted waves generated by fatigue cracks is slightly higher than that generated by reflective cracks at the interface between the asphalt surface and the semi-rigid base layer. In contrast, the amplitude of the former is significantly lower than the latter at the interface between the semi-rigid base and the roadbed. We applied these findings to the interpretation of the field GPR data of a highway pavement in China, and successfully identified the locations of the cracks and corresponding types. Our model results and field results clearly show the efficiency of our findings in the detection of cracks for highway-pavement rehabilitation.

Cracks are a common form of road distress that can significantly impact pavement integrity. Accurate detection of the attribute characteristics of cracks, including the type, location (top and bottom), width, and orientation, is crucial for effective repair and treatment. This study combines numerical simulations with filed data to investigate how the amplitudes of ground-penetrating radar (GPR) early-time signals (ETSs) vary with changes in the crack top and width, as well as how variations in the crack bottom impact radar reflected wave amplitude. The results show that when GPR ETSs are mixed with diffracted waves from the crack top, the amplitude change percentage of the ETS at the crack top exhibits a pronounced ‘∨’-shaped dip, which provides a clearer indication of the crack top. Furthermore, a positive correlation exists between crack width and the amplitude change percentage, offering a theoretical basis for quantitatively estimating crack width. On the reflected wave originating from the interface between the semi-rigid base and the subgrade, a pronounced ‘∧’-shaped dip is observed in the trough amplitude change percentage of the reflected wave at the crack bottom. For cracks of the same width, the amplitude of the ‘∧’ vertex from reflective cracks is approximately three times greater than that from fatigue cracks. This discrepancy helps identify the crack bottom and quantitatively diagnose their types. The line connecting the vertices of the ‘∨’ and ‘∧’ shapes indicate the crack’s orientation. Accurate diagnosis of crack properties can guide precise, minimally invasive treatment methods, effectively repairing road cracks and extending the road’s service life.

We have developed a stochastic inversion procedure for common-offset ground-penetrating radar (GPR) reflection measurements. Stochastic realizations of subsurface properties that offer an acceptable fit to GPR data are generated via simulated annealing optimization. The realizations are conditioned to borehole porosity measurements available along the GPR profile or equivalent measurements of another petrophysical property that can be related to the dielectric permittivity, as well as to geostatistical parameters derived from the borehole logs and the processed GPR image. Validation of our inversion procedure is performed on a pertinent synthetic data set and indicates that our method is capable of reliably recovering strongly heterogeneous porosity structures associated with surficial alluvial aquifers. This finding is largely corroborated through application of the methodology to field measurements from the Boise Hydrogeophysical Research Site near Boise, Idaho, USA.

Seismic data processing and interpretation encounter challenges related to near-surface static problems. Long-wavelength statics can be calculated using the near-surface velocity model. Although being efficient for deriving the near-surface velocity model, the first-arrival traveltime tomography (FTT) may not be sufficiently precise due to ray assumptions and limited traveltime information. The static-optimized traveltime tomography (SOTT) method has been developed to derive near-surface velocities with more accurate long-wavelength statics. However, this method does not account for short-wavelength static effects, potentially introducing artifacts into the derived velocity model. To address these limitations, we develop a novel static-constrained FTT method that simultaneously incorporates long- and short-wavelength statics. Our method introduces a static-constrained term to the objective function by minimizing the variances of static-processed traveltimes in common-offset sections. This constrained inversion framework fits the traveltime data and ensures the accuracy of the static solutions simultaneously. We determine the effectiveness of the new method by comparing it with conventional traveltime tomography and SOTT in synthetic and field data examples. The application results indicate that the static-constrained traveltime tomography has high reconstruction accuracy, especially for the high-velocity objects in the weathering layers, which are challenging to estimate by the conventional traveltime tomography. Furthermore, the static-constrained traveltime tomography mitigates the artifacts in the velocity model estimated from SOTT and achieves greater accuracy in velocity reconstructions and static calculations compared with the other two methods.

Reverse Time Migration (RTM) is an indispensable tool in the field of seismic exploration geophysical imaging as it can accurately depict underground geological structures. Nevertheless, when RTM is applied to large-scale three-dimensional complex models, it encounters serious challenges regarding the memory requirements for source wavefield reconstruction (SWR).Elastic wave RTM (ERTM) simulates the elastic wavefield by employing the time-domain finite difference method based on the standard staggered grid. Owing to the constraint of the Courant-Friedrichs-Lewy (CFL) condition, in models with large velocity differences, more refined space-time discretization is necessary. This undoubtedly further escalates the memory requirements.To address the memory problem in the spatial dimension, several methods have been developed. Boundary value methods (BVMs) and their improved versions such as one-layer BVMs, methods based on representation theorems or coefficient construction methods, and lossy compression algorithms are available. However, these methods are unable to perform memory optimization in the temporal dimension. In the temporal dimension, checkpoint methods and methods based on the Nyquist-Shannon sampling theorem can reduce the number of time steps for wavefield storage, but they ignore the memory optimization in the spatial dimension. As a result, there is a lack of a SWR strategy that optimizes memory in both spatial and temporal dimensions.We propose a SWR strategy based on the one-layer BVM and Nyquist sampling. Based on one-layer BVM, Nyquist frequency is performed for the sampling boundary values in the temporal dimension, aiming to minimize the memory requirements in both spatial and temporal dimensions as much as possible. Subsequently, three smooth and high-accuracy time-domain interpolation methods, namely global cubic spline interpolation (GCSI), piecewise cubic Hermite interpolation (PCHI), and piecewise quintic Hermite interpolation (PQHI), are utilized to reconstruct the boundary values. Numerical experiments are conducted from three aspects: boundary values, wavefield, and RTM accuracy. Numerical experimental results indicate that compared with GCSI and PQHI, PCHI can obtain migration imaging results that satisfy the accuracy requirements with lower memory requirements and higher efficiency. Compared with the conventional BVM method, this SWR strategy based on one-layer BVM and Nyquist sampling can reduce the memory requirements by approximately 200 times. This offers an important approach for large-scale RTM to get rid of inefficient disk access, or even host memory access, and achieve efficient and complete GPU memory access for RTM based on GPU parallel computing.