Search Results (677)

This study investigates the subsurface rooting of the Merija anticline in the Missour Basin, Morocco, with a focus on copper mineralization exploration. A sequential geophysical workflow was implemented, combining gravity surveys, electrical resistivity (ER), and induced polarization (IP) methods. The gravity data, acquired along spaced profiles extending from outcropping areas to Quaternary-covered zones, clearly delineated the structural continuity of the anticline beneath the cover. The application of trend filtering in covered areas allowed the removal of regional effects, successfully isolating residual anomalies associated with the buried continuation of the anticline. Interpolated Bouguer anomaly maps highlighted a major regional fault, interpreted as controlling the deep rooting of the anticline. A resistivity profile was then deployed perpendicular to this fault, providing detailed imaging of the anticline’s geometry and lithological contrasts. Complementary IP profiles conducted near the mine site targeted the detection of chargeability anomalies associated with copper mineralization dominated by malachite, confirming the electrical signature of copper mineralization, particularly within the sandstone and conglomerate formations of the Lower Cretaceous. To validate the geophysical interpretations, a drilling campaign was conducted, which confirmed the presence of the identified lithological units and the anticline rooting, as revealed by geophysical data. This approach provides a robust framework for copper exploration in the Merija area and can be adapted to similar geological contexts elsewhere. Full article

Air pollution threatens human health and ecosystems, making timely forecasting essential. The spatiotemporal dynamics of pollutants, shaped by various factors, challenge traditional methods. Therefore, spatiotemporal graph-based deep learning has gained attention for its ability to capture spatial and temporal dependencies within monitoring networks. However, many existing models, despite their high predictive accuracy, face computational complexity and scalability challenges. This study introduces clustered and lightweight spatio-temporal graph convolutional network with gated recurrent unit (ClusLite-STGCN-GRU), a hybrid model that integrates spatial clustering based on pollutant time series for graph construction, Copula-based dependency analysis for selecting relevant pollutants to predict PM_2.5, and graph convolution combined with gated recurrent units to extract spatiotemporal features. Unlike conventional approaches that require learning or dynamically updating adjacency matrices, ClusLite-STGCN-GRU employs a fixed, simple cluster-based structure. Experimental results on Tehran air quality data demonstrate that the proposed model not only achieves competitive predictive performance compared to more complex models, but also significantly reduces computational cost—by up to 66% in training time, 83% in memory usage, and 84% in number of floating-point operations—making it suitable for real-time applications and offering a practical balance between accuracy, interpretability, and efficiency. Full article

For mineral exploration, an increasing number of geophysical instruments have adopted unmanned aerial vehicles (UAVs) as their carrier platforms. The effective fusion of multi-modal geophysical information will be conducive to further enhancing the reliability of exploration results. However, the integration degree of UAVs and geophysical equipment is still low, and the advantages of UAVs as robots have not been fully exploited. In addition, the existing fusion methods are still difficult to use to establish the spatial distribution model of ore-bearing rock. Therefore, we reviewed the development status of UAVs and the geophysical instruments. We believe that only by integrating the system, designing the observation plan in accordance with the requirements of the fusion method, and treating the hardware part as an external extension of the algorithm, can high-matching data be provided for fusion. Subsequently, we analyzed the progress of the fusion methods, leading us to believe that the cross-dimensional and cross-abstract-level issues are major challenges in the algorithm aspect. Meanwhile, the fusion should be carried out simultaneously with the generation of the ore-bearing rock model, that is, to establish an integrated system of fusion and generation. It is hoped that this research can promote the development of UAV-borne multi-modal observation technology. Full article

In this study, we analyzed the lithospheric seismic background of the Tianshan and adjacent areas by combining various geophysical methods (effective elastic thickness, time-varying gravity, apparent density, and InSAR), and explored the genesis mechanism of the Wushi Ms7.1 earthquake as an example, which led to the following conclusions: (1) The effective elastic thickness (Te) of the Tianshan lithosphere is low (13–28 km) and weak, while the Tarim and Junggar basins have Te > 30 km with high intensity, and the loads are all mainly from the surface (F < 0.5). Earthquakes occur mostly in areas with low values of Te. (2) Medium and strong earthquakes are prone to occur in regions with alternating positive and negative changes in the gravity field during the stage of large-scale reverse adjustment. It is expected that the risk of a moderate-to-strong earthquake occurring again in the vicinity of the survey area between 2025 and 2026 is relatively high. (3) Before the Wushi earthquake, the positive and negative boundaries of the apparent density of the crust at 12 km shifted to be approximately parallel to the seismic fault, and the earthquake was triggered after undergoing a “solidification” process. (4) The Wushi earthquake is a leptokurtic strike-slip backwash type of earthquake; coseismic deformation shows that subsidence occurs in the high-visual-density zone, and vice versa for uplift. The results of this study reveal the lithosphere-conceiving environment of the Tianshan and adjacent areas and provide a basis for regional earthquake monitoring, early warning, and post-disaster disposal. Full article

Single-atom catalysts (SACs) have emerged as highly promising catalytic materials for the hydrogen evolution reaction (HER), attributed to their maximal atomic utilization efficiency and unique electronic configurations. Many structure parameters can influence the catalytic performance of SACs for HER, and the intrinsic advantages of SACs for HER still need to be summarized. This review systematically summarizes recent advances in SACs for HER. It discusses various types of SACs (including those based on Pt, Co, Ru, Ni, Cu, and other metals) applied in HER, and elaborates the critical factors influencing catalytic performance—specifically, the supports, coordination environments, and synergistic effects of these SACs. Furthermore, current research challenges and future perspectives in this rapidly developing field are also outlined. Full article

To study hydrogeological characteristics after the occurrence of abnormal water bursts from the weak water-rich (permeable) aquifer of the Changxing Formation limestone overlying deep working faces during production in Guizhou karst landform mining areas, hydrogeological data covering the exploration and production periods of the Xinhua mining region in Jinsha County, Guizhou Province, were collected. On the basis of surface and underground drilling, geophysical exploration techniques, empirical equations, and indoor material simulation methods, the hydrogeological evolution characteristics of the Changxing Formation limestone in the mining region after mining damage to coalbed 9 were studied. The research results indicated that the ratio of the height of the roof failure fracture zone (as obtained via numerical simulation and ground borehole detection) to the mining height exceeded 25.78, which is far greater than the empirical model calculation values (from 13.0 to 15.8). After mining the underlying coalbed 9, an abnormal water-rich area developed in the Changxing Formation limestone, and mining damage fractures led to the connection of the original dissolution fissures and karst caves within the limestone, resulting in the weak water-rich (permeable) aquifer of the Changxing Formation limestone becoming a strong water-rich (permeable) aquifer, which served as the water source for mine water bursts. Over time, after mining damage occurrence, the voids in the Changxing Formation limestone were gradually filled with various substances, yielding water storage space and connectivity decreases. The specific yield decreased with an increasing water burst time and interval after the cessation of mining in the supply area, and the correlation coefficient R was 0.964, indicating a high degree of correlation between the two parameters. Full article

This study proposes a Swin-ReshoUnet architecture with a three-level enhancement mechanism to address inefficiencies in multi-scale feature extraction and gradient degradation in deep networks for high-precision seismic exploration. The encoder uses a hierarchical convolution module to build a multi-scale feature pyramid, enhancing cross-scale geological signal representation. The decoder replaces traditional self-attention with ORCA attention to enable global context modeling with lower computational cost. Skip connections integrate a residual channel attention module, mitigating gradient degradation via dual-pooling feature fusion and activation optimization, forming a full-link optimization from low-level feature enhancement to high-level semantic integration. Simulated and real dataset experiments show that at decimation ratios of 0.1–0.5, the method significantly outperforms SwinUnet, TransUnet, etc., in reconstruction performance. Residual signals and F-K spectra verify high-fidelity reconstruction. Despite increased difficulty with higher sparsity, it maintains optimal performance with notable margins, demonstrating strong robustness. The proposed hierarchical feature enhancement and cross-scale attention strategies offer an efficient seismic profile signal reconstruction solution and show generality for migration to complex visual tasks, advancing geophysics-computer vision interdisciplinary innovation. Full article

The central and deep reservoirs of the Wushi Sag in the Beibu Gulf Basin, China, are characterized by structurally complex settings, strong heterogeneity, multiple controlling factors for physical properties of reservoirs, rapid lateral variations in reservoir thickness and petrophysical properties, and limited seismic resolution. To address these challenges, this study integrates the INPEFA inflection point technique and Morlet wavelet transform to delineate system tracts and construct a High-Frequency Stratigraphic Framework (HFSF). Sedimentary facies are identified through the integration of core descriptions and seismic data, enabling the mapping of facies distributions. The vertical constraints provided by the stratigraphic framework, combined with the lateral control from facies distribution, which, based on identification with logging data and geological data, support the construction of a geologically consistent low-frequency initial model. Subsequently, geostatistical seismic inversion is performed to derive acoustic impedance and lithological distributions within the central and deep reservoirs. Compared with the traditional methods, the accuracy of the inversion results of this method is 8% higher resolution than that of the conventional methods, with improved vertical resolution to 3 m, and enhances the lateral continuity matched with the sedimentary facies structure. This integrated workflow provides a robust basis for predicting the spatial distribution of sandstone reservoirs in the Wushi Sag’s deeper stratigraphic intervals. Full article

The Qiangtang Basin (Tibetan Plateau) poses significant geophysical challenges for seismic exploration due to near-surface widespread permafrost and steeply dipping Mesozoic strata induced by the Cenozoic Indo-Eurasian collision. These seismic geological conditions considerably contribute to lower signal-to-noise ratios (SNRs) with complex wavefields, to some extent reducing the reliability of conventional seismic imaging and structural interpretation. To address this, the common-reflection-surface (CRS) stack method, derived from optical paraxial ray theory, is implemented to transcend horizontal layer model constraints, offering substantial improvements in high-SNR prestack gather generation and prestack depth migration (PSDM) imaging, notably for permafrost zones. Using 2D seismic data from the basin, we detailedly compare the CRS stack with conventional SNR enhancement techniques—common midpoint (CMP) FlexBinning, prestack random noise attenuation (PreRNA), and dip moveout (DMO)—evaluating both theoretical foundations and practical performance. The result reveals that CRS-processed prestack gathers yield superior SNR optimization and signal preservation, enabling more robust PSDM velocity model building, while comparative imaging demonstrates enhanced diffraction energy—particularly at medium (20–40%) and long (40–60%) offsets—critical for resolving faults and stratigraphic discontinuities in PSDM. This integrated validation establishes CRS stacking as an effective preprocessing foundation for the depth-domain imaging of complex permafrost geology, providing critical improvements in seismic structural resolution and reduced interpretation uncertainty for hydrocarbon exploration in permafrost-bearing basins. Full article

Groundwater in the Qingshui River Plain of southern Ningxia is one of the main water sources for local domestic and agricultural use. However, due to the geological background of the area, 33.94% of the groundwater samples had fluoride concentrations that exceeded the WHO drinking water standards. To examine the spatial patterns and formation processes of fluoride in groundwater, researchers gathered 79 rock samples, 2618 soil samples, 21 sediment samples, 138 groundwater samples, and 82 surface water samples across the southern Qingshui River Plain. The collected data were analyzed using statistical approaches and hydrogeochemical diagrams. The findings reveal that fluoride levels in groundwater exhibit a gradual increase from the eastern, western, and southern peripheral sloping plains toward the central valley plain. Vertically, higher fluoride concentrations are found within 100 m of depth. Over a ten-year period, fluoride concentrations have shown minimal variation. Fluoride-rich rocks, unconsolidated sediments, and soils are the primary sources of fluoride in groundwater. The primary mechanisms governing high-fluoride groundwater formation are rock weathering and evaporative concentration, whereas cation exchange adsorption promotes fluoride (F⁻) mobilization into the aquifer. Additional sources of fluoride ions include leaching of fluoride-rich sediments during atmospheric precipitation infiltration and recharge from fluoride-rich surface water. Full article

Gravity and gravity gradient data serve as fundamental inputs for geophysical resource exploration and geological structure analysis. However, traditional denoising methods—including wavelet transforms, moving averages, and low-pass filtering—exhibit signal loss and limited adaptability under complex, non-stationary noise conditions. To address these challenges, this study proposes an improved U-Net deep learning framework that integrates multi-scale feature extraction and attention mechanisms. Furthermore, a Laplace consistency constraint is introduced into the loss function to enhance denoising performance and physical interpretability. Notably, the datasets used in this study are generated by the authors, involving simulations of subsurface prism distributions with realistic density perturbations (±20% of typical rock densities) and the addition of controlled Gaussian noise (5%, 10%, 15%, and 30%) to simulate field-like conditions, ensuring the diversity and physical relevance of training samples. Experimental validation on these synthetic datasets and real field datasets demonstrates the superiority of the proposed method over conventional techniques. For noise levels of 5%, 10%, 15%, and 30% in test sets, the improved U-Net achieves Peak Signal-to-Noise Ratios (PSNR) of 59.13 dB, 52.03 dB, 48.62 dB, and 48.81 dB, respectively, outperforming wavelet transforms, moving averages, and low-pass filtering by 10–30 dB. In multi-component gravity gradient denoising, our method excels in detail preservation and noise suppression, improving Structural Similarity Index (SSIM) by 15–25%. Field data tests further confirm enhanced identification of key geological anomalies and overall data quality improvement. In summary, the improved U-Net not only delivers quantitative advancements in gravity data denoising but also provides a novel approach for high-precision geophysical data preprocessing. Full article

The deep carbonate reservoirs in the Tarim Basin, Xinjiang, China, are widely developed with multi-scale complex reservoir spaces such as fractures, pores, and karst caves under the coupling of abnormal high pressure, diagenesis, karst, and tectonics and have strong heterogeneity. Among them, fracture–cavity carbonate reservoirs are one of the main reservoir types. Revealing the petrophysical characteristics of fracture–cavity carbonate reservoirs can provide a theoretical basis for the log interpretation and geophysical prediction of deep reservoirs, which holds significant implications for deep hydrocarbon exploration and production. In this study, based on the mineral composition and complex pore structure of carbonate rocks in the Tarim Basin, we comprehensively applied classical petrophysical models, including Voigt–Reuss–Hill, DEM (Differential Effective Medium), Hudson, Wood, and Gassmann, to establish a fracture–cavity petrophysical model tailored to the target block. This model effectively characterizes the complex pore structure of deep carbonate rocks and addresses the applicability limitations of conventional models in heterogeneous reservoirs. The discrepancies between the model-predicted elastic moduli, longitudinal and shear wave velocities (Vp and Vs), and laboratory measurements are within 4%, validating the model’s reliability. Petrophysical template analysis demonstrates that P-wave impedance (Ip) and the Vp/Vs ratio increase with water saturation but decrease with fracture density. A higher fracture density amplifies the fluid effect on the elastic properties of reservoir samples. The Vp/Vs ratio is more sensitive to pore fluids than to fractures, whereas Ip is more sensitive to fracture density. Regions with higher fracture and pore development exhibit greater hydrocarbon storage potential. Therefore, this petrophysical model and its quantitative templates can provide theoretical and technical support for predicting geological sweet spots in deep carbonate reservoirs. Full article

Strapdown gravity systems are increasingly employed in airborne geophysical exploration and geodetic studies due to advantages such as ease of installation, wide dynamic range, and adaptability to various platforms, including airplanes, helicopters, and large drones. This study presents results from an airborne gravity survey conducted over the northeastern sector of Sardinia (Italy), using a high-resolution strapdown gravity ensuring an accuracy of approximately 1 mGal. Data were collected at an average altitude of 1800 m with a spatial resolution of 3.0 km. The survey focused on the Sos Enattos area near Lula (Nuoro province), a candidate site for the Einstein Telescope (ET), a third-generation gravitational wave observatory. The ideal site is required to be geologically and seismically stable with a well-characterized subsurface. To support this, we performed a new gravity survey to complement existing geological and seismic data aimed at characterizing the mid-to-shallow crustal structure of Sos Enattos. Results show that the strapdown system effectively detects gravity anomalies linked to crustal sources down to ~3.5 km, with particular emphasis within the 1–2 km depth range. Airborne gravity data reveal higher frequency anomalies than those resolved by the EGM2008 global gravity model and show good agreement with local terrestrial gravity data. Forward modeling of the gravity field suggests a crust dominated by alternating high-density metamorphic rocks and granitoid intrusions of the Variscan basement. These findings enhance the geophysical understanding of Sos Enattos and support its candidacy for the ET site. Full article

This paper presents a compact, high-efficiency electromagnetic transmitter for Controlled-source Audio-frequency Magnetotelluric (CSAMT) applications, operating in the 10–100 kHz range. A novel bipolar square-wave inverter topology is proposed, which directly modulates the transformer’s secondary-side AC output, eliminating conventional rectification and filtering stages. This design reduces system losses (simulated efficiency > 90%) and achieves an approximately 40% reduction in both volume and weight. The power stage uses a full-bridge bipolar inverter topology with SiC-MOSFETs, combined with a high-frequency transformer for voltage gain. Simulation, laboratory testing, and EMI evaluation confirm stable square-wave generation and full compliance with EN55032 Class A standards. Field validation with a CSAMT receiver demonstrates effective signal transmission and high-resolution subsurface imaging, thereby improving the efficiency and portability of urban geophysical exploration. Full article

The quantitative characterization of near-surface heterogeneity using ground-penetrating radar (GPR) is an important but challenging task. The estimation of subsurface geostatistical parameters from surface-based common-offset GPR reflection data has so far relied upon a Monte-Carlo-type inversion approach. This allows for a comprehensive exploration of the parameter space and provides some measure of uncertainty with regard to the inferred results. However, the associated computational costs are inherently high. To alleviate this problem, we present an alternative deep-learning-based technique, that, once trained in a supervised context, allows us to perform the same task in a highly efficient manner. The proposed approach uses a convolutional neural network (CNN), which is trained on a vast database of autocorrelations obtained from synthetic GPR images for a comprehensive range of stochastic subsurface models. An important aspect of the training process is that the synthetic GPR data are generated using a computationally efficient approximate solution of the underlying physical problem. This strategy effectively addresses the notorious challenge of insufficient training data, which frequently impedes the application of deep-learning-based methods in applied geophysics. Tests on a wide range of realistic synthetic GPR data generated using a finite-difference time-domain (FDTD) solution of Maxwell’s equations, as well as a comparison with the results of the traditional Monte Carlo approach on a pertinent field dataset, confirm the viability of the proposed method, even in the presence of significant levels of data noise. Our results also demonstrate that typical mismatches between the dominant frequencies of the analyzed and training data can be readily alleviated through simple spectral shifting. Full article