The unmanned aerial vehicle (UAV)-based ground-penetrating radar (GPR) migration imaging encounters substantial challenges in practical applications due to various noise interferences. This study presents a GPU-accelerated imaging algorithm that integrates reverse time migration and total variation denoising (RTM-TVD) to enhance the image quality of UAV-based GPR migration profiles. By employing two acceleration strategies, this algorithm achieves a tenfold increase in speed compared to the traditional RTM method. Additionally, a quantitative evaluation is conducted to assess the effects of terrain roughness, flight altitude (FA), and noise levels (NLs) on imaging quality. This evaluation utilizes two key metrics: image entropy and the structural similarity index (SSIM). Our findings reveal that the relationship between terrain roughness and NLs with entropy values follows a positive linear trend, whereas a negative linear trend is observed in SSIM values. The advantages of the RTM-TVD approach are validated through field GPR data collected from the Lemon Creek Glacier in the U.S.

Current forward modeling approach for three-dimensional (3-D) controlled-source electromagnetic (CSEM) problems in anisotropic media exhibit limited capability in addressing large-scale models. Given that, we have developed an innovative 3-D forward modeling algorithm for anisotropic CSEM problems by using Fourier finite element method (FFEM). Starting from the Maxwell’s equations, the 3-D governing equations of the secondary vector and scalar potentials based on the Coulomb gauge are derived. That secondary field approach can not only effectively eliminate source singularities, but also maintain flexibility for various source configurations. Then, a horizontal two-dimensional (2-D) Fourier transform is employed to convert the 3-D governing equations from the spatial domain into a system of one-dimensional (1-D) ordinary differential equations in the space-wavenumber domain. The resulting equations are discretized and solved via the 1-D finite element method (FEM). Additionally, a contraction operator is applied to iteratively correct the total electric field. By leveraging the computational efficiency of Fourier transform and the stability of iterative techniques, the proposed approach achieves enhanced computational efficiency without compromising numerical accuracy. The validity, performance and convergence of the proposed approach was verified by using 1-D isotropic canonical off-shore hydrocarbon model and 3-D anisotropic prism model. Compared with the open source package PETGEM based on the high-order edge finite element method, the proposed approach using FFEM exhibits obvious efficiency advantages for 1-D isotropic canonical off-shore hydrocarbon model. Finally, two sophisticated geological models were employed to validate the robustness of FFEM in handling complex subsurface conditions, and the influence of frequency on electromagnetic field distribution was also investigated.

Traditional magnetotelluric (MT) denoising methods often encounter limitations in various scenarios. However, with its robust adaptability and high precision, deep learning has exhibited outstanding denoising performance when applied to MT exploration time series data. Recent researches have mainly focused on developing advanced single deep learning models to enhance MT denoising effectiveness. This paper introduces a lightweight ensemble learning approach for MT denoising, aiming to enhance denoising performance via a single deep convolutional network. Our ensemble learning strategy uses a sliding window technique to generate overlapping MT time series segments, thereby providing multiple inputs for a specialized noise-fitting network. This variety of inputs enables a comprehensive understanding of MT data, thereby increasing the probability of identifying complex noise patterns. Then, the outputs from these inputs are integrated using a method that combines shifting averages and adaptive thresholding to obtain more accurate fitted noise contours. Furthermore, we apply a three-layer Density-Based Spatial Clustering of Applications with Noise (DBSCAN) methodology to identify the real noise contours among the fitted noise contours, and then to get the residual signal by subtracting those real noise contours. Subsequently, the residual signal is further processed by the pre-trained denoising network to eliminate noise artifacts. The efficacy of our approach is validated through experiments conducted with both synthetic and field data, demonstrating substantial improvements in denoising, particularly within mid and low frequency ranges. Several interrelated parameters exhibit notable improvements, including apparent resistivity and phase curves, time-frequency domain curves, and so on.

Many rocks exhibit electrical anisotropic characteristics, leading to artifacts in isotropic inversion of dc resistivity data. To mitigate this issue, we employ the flexible unstructured finite element (FE) method for 3-D anisotropic forward modeling and anisotropic inversion for dc resistivity data. Synthetic inversions further validate algorithm feasibility. The code not only replicates the inversion of principal axis resistivities accomplished by previous researchers but also reconstructs challenging all angle parameters. Comparative analysis of inversion using isotropic and arbitrary anisotropic assumptions reveals that anisotropic inversion accurately recovers the parameters. Models comprising conductive and resistive targets, respectively, are utilized to stimulate two realistic scenarios. The angle parameters of conductive target are challenging to recover compared to a conductive one with the same anisotropy. We conduct comprehensive experiments to assess the results across various data types. Borehole-generated data enhances the principal resistivities resolution, aligning with the direction of the lines connecting to the boreholes, rather than all ones, whereas conventional surface configurations are unable to accurately capture discrepancies between principal resistivities and Euler angles. Moreover, the investigations confirm the resolvability of each anisotropy component, especially angle parameters, which depend on borehole distribution. These experiments on data-acquisition configurations greatly enhance the practical possibility of successfully collecting data containing anisotropy information, achieving reconstruction of angular parameters, and obtaining better images of principal axis resistivity in practical applications. Even with the correct anisotropic assumption, unreasonable data-acquisition configurations produce inversion results with distortion. Testing with the model containing multiple anisotropic targets further confirms the algorithm’s effectiveness.

Three-dimensional forward modeling algorithms of conductivity anisotropy with respect to the magnetotelluric method have been widely developed. However, numerical modeling considering the magnetic susceptibility anisotropy is less studied, and the magnetic susceptibility of the underground medium cannot be ignored in some areas of magnetite-rich rocks. In this study, the nodal finite element method and the unstructured tetrahedral grid were utilized to discretize the A-ϕ system for a model with both conductivity and magnetic susceptibility anisotropies, which was solved using the biconjugate gradient stabilized method. By comparing the numerical results with the analytical solutions of two-layered models and a previous vector finite element solution of a single-block model, the accuracy of the implemented algorithm was verified. Then, the influence of magnetic susceptibility anisotropy on magnetotelluric responses was analyzed based on different block models as well as on a quasi-two-dimensional model producing out-of-quadrant phases. Different types of MT data were considered to conduct these analyses, including the apparent resistivity, impedance phase, phase tensor, and tipper. Our findings point to the same conclusion that the effect of magnetic susceptibility anisotropy cannot be ignored in magnetite-rich areas.

At present, the three-dimensional (3D) magnetotelluric (MT) unstructured finite element modelling approaches often adopt the block-wise uniform conductivity parameterization method, in which the Earth conductivity model is discretized into plenty of tetrahedrons with uniform conductivity. However, that block-wise uniform parameterization method is not only difficult to accurately simulate actual continuous subsurface conductivity distribution, but also would substantially increase the unknowns in the subsequent inversion, which largely aggravates the non-uniqueness of inversion. In this study, we propose a three-dimensional unstructured finite element modeling strategy of magnetotelluric problems allowing for continuous variation of conductivity in each tetrahedron. First, the curl-curl governing differential equation of electrical field for the 3D MT problems and the corresponding integral weak form are derived successively. Next, the open-source tetrahedral mesh generator Tetgen is utilized to discretize the computational domain, both the conductivity and the electric field within each tetrahedral element are approximated by using linear interpolation with node and edge vector basis functions, respectively. Then, the closed-form expression of the resulting elemental integral is derived and the Newman type boundary condition is loaded. The resulting system of linear equations is solved by using the direct LU decomposition method. Further, to enhance the accuracy of the solution, a goal-oriented adaptive technology based on the continuity of the normal component of the current density is adopted. Finally, the correctness of the proposed algorithm is verified by using a layered model with continuous conductivity distribution and a 3D topographic model. While the necessity of considering the continuous variation of conductivity in blocks is proved by using two 3D continuum models. The results show that the proposed algorithm can obviously improve the accuracy of the responses at the sites above the region with continuous conductivity distribution and will greatly reduce the unknowns of the subsequent inversion, compared with the traditional algorithm with block-wise uniform parameterization.

Galvanic distortion resulting from near-surface heterogeneity has long been considered a major impediment to the accurate interpretation of magnetotelluric data. As this galvanic distortion effect is approximated by a real tensor, it can be solved numerically in the three-dimensional magnetotelluric inversion. To achieve this goal in a practical way, we have implemented the inverse solution of both the galvanic distortion and resistivity parameters in the widely used software package ModEM. To address the gradient domination problem of the distortion parameter over resistivity, a scaling scheme is proposed to balance their separate roles in the total gradient. Two synthetic datasets of the Block2-3D and the Oblique Conductor models provided in ModEM package were distorted and then used to validate the new implementation. Synthetic inversions demonstrate that the artifacts introduced by fitting the galvanic distortion effect can be removed and the regional structure is better resolved. A field data inversion test was conducted on the magnetotelluric data from the Red River Fault zone and its adjacent areas on the southern margin of the Tibetan Plateau. Using the distortion-free phase tensor, we show that the new inversion significantly removes the galvanic distortion artifacts from the shallower structure. It obtained a slightly different crustal structure and a clearer delineation of the upper mantle structure compared to the previous study.

Conventional resistivity inversion methodologies encounter constraints in perpetual monitoring, owing to the necessity for recurrent measurements. In response, this research leverages a 3D finite element method to formulate an approximate geometry imaging of cross-borehole resistivity during forward modelling, circumventing the direct computation of Jacobian matrix equations in the electric field. This study meticulously explores the complex relationship among apparent resistivity (ρa), CO2resistivity (ρCO2), and the volume of the CO2storage area (VCO2). Remarkably, the impact of ρCO2on ρais found to be more pronounced than that ofVCO2, attributed to the repulsion effect emanating from the high-resistance storage area. A robust linear correlation between ρaandVCO2is identified across various multi-horizontal layer models, while the relationship between ρaand ρCO2adheres to a rational function. The intricate correlation between ρaand CO2concentration is dissected, offering a quantitative perspective for inferring the resistivity of the CO2storage area. These findings are further validated through field formation models featuring salt caverns, highlighting the effectiveness of cross-borehole resistivity imaging for CO2storage monitoring. Beyond enhancing our understanding of subsurface geological behaviour, our study underscores the feasibility of utilizing salt caverns for CO2storage, presenting a pioneering approach towards navigating the monitoring of subsurface CO2storage.