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.

Named Data Network (NDN), as a promising information-centric networking architecture, is expected to support next-generation of large-scale content distribution with open in-network cachings. However, such open in-network caches are vulnerable against Cache Pollution Attacks (CPAs) with the goal of filling cache storage with non-popular contents. The detection and defense against such attacks are especially difficult because of CPA's similarities with normal fluctuations of content requests. In this work, we use a clustering technique to detect and defend against CPAs. By clustering the content interests, our scheme is able to distinguish whether they have followed the Zipf-like distribution or not for accurate detections. Once any attack is detected, an attack table will be updated to record the abnormal requests. While such requests are still forwarded, the corresponding content chunks are not cached. Extensive simulations in ndnSIM demonstrate that our scheme can resist CPA effectively with higher cache hit, higher detecting ratio, lower hop count, and lower algorithm complexity compared to other state-of-the-art schemes.

Collaborative filtering (CF) is widely used in recommendation systems. Traditional collaborative filtering (CF) algorithms face two major challenges: data sparsity and scalability. In this study, we propose a hybrid method based on item based CF trying to achieve a more personalized product recommendation for a user while addressing some of these challenges. Case Based Reasoning (CBR) combined with average filling is used to handle the sparsity of data set, while Self-Organizing Map (SOM) optimized with Genetic Algorithm (GA) performs user clustering in large datasets to reduce the scope for item-based CF. The proposed method shows encouraging results when evaluated and compared with the traditional item based CF algorithm.