Wenxin Kong

We developed a multi‐module convolutional neural network combined with a multi‐head attention mechanism (MM‐CNN‐MHA) to forecast geomagnetically induced currents (GICs) at the Huangmeishan (HMS) 220‐kV substation in Fujian Province, China. The model was trained using solar wind parameters, geomagnetic disturbances, and geoelectric field observations from 2010 to 2021. Five GIC estimation or prediction methods were evaluated: (a) GICs calculated from geoelectric fields predicted by MM‐CNN‐MHA; (b) direct GICs prediction using MM‐CNN‐MHA; (c) GICs calculated from measured geoelectric fields; (d) GICs calculated using a one‐dimensional (1D) conductivity model; and (e) GICs calculated using magnetotelluric (MT) impedance data. The results show that the GICs calculated from measured geoelectric fields achieved the highest accuracy, with a root‐mean‐square error (RMSE) of 0.24 A, and better reproduced the observed GICs peaks. The GICs calculated from MM‐CNN‐MHA‐predicted geoelectric fields outperformed the directly predicted GICs, with RMSE values of 0.26 and 0.28 A, respectively. In contrast, the GICs calculated using the 1D conductivity model and MT impedance method generally overestimate GICs peaks, with RMSE values of 0.52 and 0.39 A, respectively. These findings further highlight the importance of local geoelectric field observations for GIC monitoring and forecasting and demonstrate that preserving the physical chain from solar wind, geomagnetic disturbances, and geoelectric fields to GICs improves the stability and reliability of GIC predictions.

Geomagnetic disturbances, as an important manifestation of space weather, not only affect the geospace environment but may also influence near-surface meteorological variables. However, the relationship between the two remains to be further investigated. This study employs a mathematical model that couples geomagnetic disturbances with terrestrial meteorological conditions to systematically analyze the correlations between geomagnetic disturbances and both temperature and wind speed. The validity of the model is verified through comparison with existing research findings. Taking the 500 kV transmission lines in Guangxi, China, as the study object, the correlations between geomagnetic disturbances and terrestrial weather are analyzed using data from the geomagnetic storm events on March 16–19, 2015, and May 10–13, 2024, as well as data on the once-in-50-year extreme meteorological conditions in Guangxi. The results indicate that wind speed is generally significantly positively correlated with the intensity of geomagnetic disturbances, while in cold winter regions, geomagnetic disturbances tend to be negatively correlated with temperature, consistent with findings based on statistical methods.

Geomagnetically induced currents (GICs) pose a significant threat to the safe and stable operation of power systems, making accurate risk assessment under extreme geomagnetic storm conditions essential. This study focuses on the mid‐latitude North China power grid and estimates substation GICs in the 1,000 kV network during the geomagnetic storm from 9 to 15 May 2024. A three‐dimensional (3D) anisotropic resistivity model is constructed to investigate the impact of electrical anisotropy on GIC estimation. For comparison, a 3D isotropic resistivity model and observed magnetotelluric (MT) impedance data are also employed, together with a one‐dimensional (1D) resistivity model from previous studies, to analyze the GIC responses during this storm systematically. The results show that, compared with the 3D isotropic model, incorporating anisotropy can lead to GIC differences of up to 90 A at certain substations. When using the 1D model and observed MT impedance data, the GIC differences at individual substations can exceed 300 A. In addition, GIC estimates derived from MT data differ from those based on the two 3D resistivity models by more than 300 A. A statistical analysis based on MT observation sites identifies six high‐risk substations, primarily located in the southern North China Plain and the Lvliang Mountains and Taihang Mountains.

The three-dimensional arbitrary anisotropic inversion algorithm for direct current (DC) resistivity method is developed based on unstructured meshes and L-BFGS optimization, enabling the simultaneous recovery of all independent anisotropic parameters. To enhance robustness and decrease inversion non-uniqueness, distinct meshes are employed for forward modeling and inversion. A novel mesh-assignment strategy is introduced, where inversion meshes for individual anisotropy parameters are aligned specifically with the directional resolution inherently provided by the surficial data. This approach eliminates the conventional requirement for symmetric surficial configurations in three-dimensional DC resistivity anisotropic inversion based on unstructured meshes. Furthermore, to stabilize inversion updates, a volume-weighted method is implemented to compute the objective function gradient on the coarse inversion mesh by aggregating contributions from the finer mesh. This prevents the gradient of one particular parameter from dominating the optimization process due to unbalanced accumulation, a common issue in traditional direct summation strategy, resulting in a more reasonable update of anisotropy parameters and ensuring the reliable recovery of principal resistivities and orientation angles. Five anisotropic inversions with distinct mesh strategies were performed for each model. Comparative analysis based on synthetic data confirms the effectiveness of the proposed approach in improving anisotropic inversion accuracy and resolution, especially in distinguishing between principal resistivities and angle. These results validate both the practicality and reliability of the proposed approach.

Geomagnetic storms can induce geomagnetically induced currents (GIC) in power systems, posing threats to grid security. In this study, a GIC calculation model for the 500 kV power grid in Guangxi, China, is developed based on one‐dimensional (1D) and various three‐dimensional (3D) magnetotelluric (MT) impedance data. The geomagnetic disturbance data of the “solar superstorm” on July 23–24, 2012, were simulated using the Space Weather Modeling Framework. Together with five real geomagnetic storm events, these data were used to estimate the GIC responses at each substation. The results show that the substation GIC peak calculated using the simulated geomagnetic disturbance data in July 2012 is as high as 243 A, which is comparable to the GIC peak calculated during the geomagnetic storm from October 29 to 31, 2003. Some substations experienced GIC exceeding 300 A during the geomagnetic storm on May 10–13, 2024. When the geomagnetic storm intensity reaches the level of the October 29–31, 2003 event, the differences in GIC estimates between 1D and 3D MT impedance data can be as large as 300 A. When storm intensity reaches the level of the May 10–13, 2024 event, the differences in GIC estimates derived from different 3D MT impedance data can be as much as 100 A. The GIC risk assessment identified six high‐risk substations, all located in southeastern Guangxi. Among them, three coastal substations are recommended for prioritized deployment of GIC monitoring devices.

We propose a new three-dimensional arbitrary anisotropic inversion algorithm for controlled-source audio-frequency magnetotellurics with consideration of angle parameters. In this method, the study area is discretized using unstructured tetrahedral meshes, and vector basis functions are employed to interpolate electric and magnetic field values at the receiver locations. The anisotropic resistivity in the governing electromagnetic equations is described using a tensor, and parameterized by three principal resistivities and three Euler angles. The inversion problem is solved by the L-BFGS optimization scheme to efficiently find a reasonable model. We designed three representative types of synthetic models to investigate and test the algorithm: (1) anisotropic targets embedded in isotropic surrounding rocks, (2) isotropic targets surrounded by anisotropic media, and (3) complex models in which both targets and surrounding rocks exhibit anisotropy. The results demonstrate that the proposed inversion method can successfully recover these two horizontal principal resistivities—especially addressing the gap left by previous studies, as well as their corresponding rotation angles. For the second type of model, we further examined the influence of various initial models, particularly on the recovery of anisotropy angle parameters. Finally, by the separate inversion using the diagonal and off-diagonal components of the impedance tensor, we confirm the crucial role of off-diagonal components in the accurate reconstruction of the anomaly's shape and, especially, the angle parameters.

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.

The Eryuan–Yangbi seismic zones in northwestern Yunnan Province, China, feature intersecting faults and geothermal activity. Persistent yet intermittent earthquakes occur here, though not linked to known faults, suggesting a novel mechanism. A 3–D resistivity model reveals that seismic events align with low‐resistivity zones in the middle‐upper crust. Middle‐crust anomalies indicate crustal melts, while shallow anomalies likely represent saline fluids released from the melts. Fluid accumulation increases pore pressure, weakening faults and triggering slip. Once a critical stress threshold is reached, fault‐valve behavior enhances permeability, facilitating fluid migration and seismic activity. Following earthquakes, permeability decreases as fractures seal, initiating a new cycle of fluid buildup and stress accumulation. Continuous crustal flow from the Tibetan Plateau replenishes the melts, driving ongoing seismicity in the region.

Determining the water content in the lithospheric mantle is crucial for understanding its dynamic evolution. Because the electrical conductivity of mantle minerals is particularly sensitive to water, the magnetotelluric (MT) method becomes a vital tool to determine the water content in the lithospheric mantle. Here we used broadband and long‐period MT data collected along a 600‐km‐long, NS‐trending profile to obtain the electrical resistivity structure of the lithosphere across the southwestern South China Block. By combining the results of laboratory electrical conductivity measurements of mantle minerals, xenolith‐derived composition, and geotherm information, we further estimated the water content of the lithospheric mantle. The results show that the Youjiang Basin has a relatively thin lithosphere segmented by zones of low‐resistivity that spatially coincide with major faults. The relatively conductive mantle lithosphere could be explained by the combined effects of water in nominally anhydrous minerals, sulfide and phlogopite. Combined with regional tectonic context, we proposed that H ₂ O‐rich fluids derived from the previously subducted slabs and related metasomatic processes lead to lithospheric hydration and thinning within the Youjiang Basin. Additionally, such processes, together with magmatic‐hydrothermal activities, likely contribute to the formation of gold deposits within the basin. By contrast, the lithosphere beneath the Yangtze Craton is characterized by high resistivity extending to a depth of ∼200 km, representing a typical cratonic lithosphere that has not undergone significant tectonic modification and contains no or very little water.