In recent years, deep learning methods have been playing an important role in extracting features from Ground Penetrating Radar (GPR) images to detect underground targets rapidly. However, the shortage of GPR training data and the limited transferability of supervised deep learning models bring difficulties in generalizing the applications. To overcome the training-data dependency and enhance the model transferability, we propose an Unsupervised GPR Target Clustering Method based on the Contrastive Language–Image Pre-training (UGTC-CLIP) Model to achieve a rapid target classification. The experiment results indicate that the proposed method can effectively classify different GPR targets with an accuracy of 0.88 without fine-tuning, which is comparable to the performance of the trained Vision Transformers (ViT) model. Furthermore, the remarkable transferability of the model enables rapid adaptation to new samples or different geological conditions of GPR civil detection scenarios. This proposed unsupervised classification method is expected to be an efficient solution for real-time GPR data interpretation in transportation infrastructure inspections.

The prediction of physical properties for porous medium plays an essential role in geological resource exploration and subsurface exploitation. Deterministic methods based on computational or numerical experiment provide an efficient way to estimate the physical properties of porous medium. However, uncertainty and randomness are common characteristics in the geological system. The variability of physical property in porous medium cannot be explicitly explained by a limited number of models. In this article, we propose an interval estimator-based Bayesian evidential learning (IE-BEL) framework to quantify the uncertainty while predicting physical properties of porous medium at the same time. First, we utilize a stochastic simulation to generate a number of high-quality 3-D models. Second, the morphological characteristics and physical properties are numerically computed as the training data. Third, a combination of machine learning techniques, including eXtreme gradient boosting (XGBoost) and model agnostic prediction interval estimator (MAPIE), is employed to obtain reliable interval predictions with uncertainty quantification. Fourth, a model calibration process is conducted to regulate the physical property predictions. We validate the proposed method by three practical examples, including both artificial and natural porous materials. Compared with the previous method, the IE-BEL framework shows competitive performance in predicting the physical properties of porous medium. The experiment result indicates that the proposed method can address the uncertainty quantification problem associated with physical properties prediction.

Fracture networks are omnipresent in unconventional energy reservoirs. The inversion of fractures is of vital importance to oil and gas exploration and production. Most of the existing inversion methods are developed based on homogeneous media theory and rely on a solitary physical descriptor. For instance, one commonly employed single-property inversion approach is the determination of water saturation through the use of the media’s electrical conductivity. With the fast development of multiphysics geological survey, a joint inversion framework that is suitable for anisotropic fractured media is needed. In this article, we propose an electrical–elastic joint inversion method involving both electrical tensor and elastic tensor to invert the fracture characteristics (e.g., fracture shape, inclination angle, and porosity). We conduct numerical experiments with two-phase geometries containing idealized ellipsoidal fractures. The resistivity tensor and Young’s moduli of different directions are calculated and used to construct an anisotropy diagram and a joint inversion chart. The method is validated by comparing the predicted fracture geometry with the actual geometry of the fracture embedded in media. Both ideal homogeneous media and digital rock samples are used to test the inversion framework. A comparison between the single- and the joint-property inversion is also presented, and the joint-property inversion shows a higher accuracy in predicting fracture volume and tilting angle. This work indicates that the proposed electrical–elastic joint method can capture the anisotropy of the formation rock, and the multiphysics inversion framework exhibits the potential to recover fracture features with high fidelity.

In oil and gas exploration, formation water (or hydrocarbon) content estimation is essential for reservoir evaluation, development, and production. Archie’s law, which associates the formation resistivity and water saturation, has been widely adopted for reservoir assessment. However, the accuracy of the scalar-based Archie’s law falls when the formation exhibits strong heterogeneity (e.g., fractured shales), as the electrical anisotropy is neglected in the scalar model. In this letter, we propose a tensorial Archie’s law based on the effective resistivity tensor of the formation. We construct numerical experiments of idealized three-phase formation geometries that contain ellipsoidal inclusions. The resistivity tensor is calculated from the simulation results and used in the newly proposed Archie’s law to calculate the water saturation of the formation, and the model is validated by comparing the predicted saturation with the calculated value from the known geometries. The results show that the tensorial Archie’s law captures the anisotropy of the formation by including all tensor elements of the resistivity, thus improving the predictability.

Remote sensing measurements of Earth materials are always made at scales much larger than individual grains and cavities, yielding only upscaled effective properties. An “effective medium” is an idealized uniform material that has the same measured properties as the real mixture. A uniform electric field applied to the ideal effective medium remains uniform within the sample; however, the same electric field applied to the composite results in fine-scale spatial variations of field strength within the sample, which depend on the properties of the constituents, their volume fractions, and their microgeometries. We derived analytic expressions for the electric field strength heterogeneity implicit in commonly used dielectric effective medium models. Only two-phase, statistically isotropic, low-loss materials, e.g., ice, snow, minerals, and freshwater in the microwave UHF band are considered. The method applies to singly or biconnected phases. The results confirm the uniform field in the isolated phase of material lying on the Hashin-Shtrikman (HS) bounds; the continuous phase field variance increases with a decreasing volume fraction, approaching a well-defined limit as the fraction becomes vanishingly small. Expressions are found for field variance in higher-order composites of coated spheres, providing realizations of composites lying between the HS bounds, and illustrating field nonuniqueness when microstructure is unknown. The mean and variance of the field strength in popular effective medium models are also examined. Not only do the effective properties predicted by these models differ so do the electric field strength spatial variability, especially when the volume fraction of inclusions increases.

Dielectric properties of rocks are the important indicators of subsurface formations when electromagnetic sensing methods are applied. Interpreting those in situ measurements relies on characterizing the dielectric permittivity of rock samples in the laboratory. For solid phase samples, the parallel-plate capacitance method (PCM) serves as one of the most accurate and feasible methods for charactering electrical properties. Low-frequency (~kilohertz) PCM measurements are most common; extending the PCM to an ultra-wideband measurement will establish a general framework for measuring both low-frequency and high-frequency (~gigahertz) properties (e.g., permittivity and conductivity). Major challenges of ultra-wideband measurements using the PCM are: 1) at high frequency, the measurement apparatus may introduce errors due to reflection and dissipation of the electromagnetic waves and 2) resonance caused by impedance mismatching in the apparatus design can occur at higher frequency, which will significantly compromise the accuracy of this method. Thus, the optimization and modification of the method is needed. In this letter, we: 1) show the validity and accuracy of the PCM for permittivity measurement by comparing numerical simulation and experiments; 2) propose a practical geometric parameter to conduct system-level optimization under giving constrains; 3) perform the optimization for the sample holder to increment the measurement accuracy at higher frequency without downsizing the sample. The results show that a satisfactory accuracy and stability can be achieved in an ultra-wide range of frequency spectrum from 100 kHz to 1.5 GHz with the improved PCM apparatus design.

A common practice for estimating subsurface constituents from remote sensing methods is to use analytical effective medium models relating effective dielectric permittivity to properties of the targeted region. These models suggest that the effective permittivity depends on the volumetric fraction and phase property of each constituent of the composite. Most effective medium models are based on idealized approximations of the composite's geometry. Some studies have shown that the analytical mixing rule may underestimate or overestimate the effective property when there are geometrical variations. In this paper, we use numerical experiments to compute the effective dielectric permittivity of composites with different microgeometries having varying amounts of internal interfaces. By comparing the numerical results with the classic analytical mixing rules, we quantify the discrepancy with a diagram that indicates the high deviation region. The study is carried out by using various fluid-solid permittivity contrasts to suit a wide range of applications.

Pavement life span is often affected by the amount of voids in the base and subgrade soils, especially the soil moisture content. Ground Penetrating Radar (GPR) is one of the desirable techniques to indirectly measure the in-situ soil moisture content through electrical properties of soils. The inversion using transmission line matrix method from GPR data is applied for converting moisture content of the soils. Laboratory and field tests proved satisfactory results.

Electrical properties of sediment rocks is essential to the data interpretation of electrical and electromagnetic logging. An optimized ultra-wideband parallel-plate capacitor device is proposed to achieve the electrical parameters measurement. The validity and accuracy of the proposed measurement system is verified by experimental results. The results show that a better accuracy and stability of the measurement system can be achieved with the optimized parallel-plate capacitor model while the upper limit of measurement frequency can reach 1.5 GHz.

A ground-wave technique is introduced in this letter to directly extract the pavement permittivity and thickness from measured data of ground-coupled ground-penetrating radar (GPR). Analytic solution, numerical simulation, and experimental test are carried out to validate the method. This technique enables bistatic radar to obtain both thickness and permittivity by just one measurement, which effectively reduces measurement and computation time for GPR applications.

A numerical evaluation of the permittivity of sandstones through the micro Computerized Tomography (micro CT) images is conducted using an image porosity extracting algorithm and an improved Finite Difference Method (FDM). Within the acquired physical properties by 3D micro CT scanning, numerical method is used to model and compute the permittivity of the rock samples. The simulated results of 2 clastic sandstone samples with dry state and saturated state are compared with experimental data for validating the accuracy of the proposed numerical method. The results show great agreement and the error of permittivity evaluation is less than 3%.

A borehole imaging method for nonconductive fluid application using a high-frequency electromagnetic (EM) short pulse is introduced in this letter. The pulsed borehole imaging system offers more advantages when compared with conventional dielectric or resistivity imaging tools. The continuous measurement in a wideband spectrum improves accuracy and provides comprehensive well-bore formation analysis information. A high resolution in both vertical and horizontal directions for well-bore cracks, rugosity, and dielectric dispersion of formations can be obtained by radiating an EM pulse signal with a center frequency of 1.2 GHz into a well-bore via a wideband antenna system. This design has been proven to work effectively in oil-based mud and may be used as an alternative tool for logging while drilling imaging applications. By using the transmission line matrix method, numerical simulation results are presented to verify the performance of the proposed EM pulse imaging system. An improved wideband bow-tie antenna is designed for a proposed multispacing EM pulsed imaging system as well.