Search Results (6,663)

The West Junggar region in Xinjiang, NW China, hosts more than 100 gold deposits, most of which are shallow and nearing depletion. To assess deep mineralization potential, we integrated in situ sulfur isotope geochemistry with audio-frequency magnetotelluric (AMT) surveys at three representative deposits (Hatu, Baogutu, and Baogutu XI). Sulfide δ³⁴S values (0.46–4.16‰) indicate a deep magmatic–hydrothermal source. Petrophysical measurements reveal systematic resistivity contrasts that correlate with sulfide content. AMT surveys effectively delineate low-resistivity anomalies corresponding to mineralized zones, with persistent anomalies extending beneath known orebodies and along fault belts. These anomalies display two distinct geometric patterns: steeply dipping faults with en echelon fractures (Hatu) and S-shaped dip-transition zones (Baogutu and Baogutu XI), both reflecting structural controls on mineralization. The identified anomalies define probable mineralized zones at depth, suggesting significant undiscovered potential. This integrated geochemical and geophysical evidence provides compelling targets for deep exploration in the West Junggar region. Full article

Non-destructive characterization of electromagnetic (EM) wave propagation properties in drill cores is gaining prominence as a foundation for reliable geophysical inversion, improved rock-physics modeling, and increasingly data-driven mineral exploration workflows. Lab-based rock characterization requires benchmarks that link the density, elastic, electrical, magnetic, and EM properties of studied cores to lithology and mineralization, enabling more accurate interpretation of geophysical data. This study develops a robust high-frequency EM (HFEM) wave velocity measurement technique and incorporates it within a standardized non-destructive framework validated across multiple mineral systems in Newfoundland and Labrador, Canada. The developed method derives EM velocities from two-way travel time through drill cores positioned above a metallic reflector, supported by finite-difference time-domain simulations to optimize antenna frequency and test geometry. A repeatable signal-processing workflow was implemented to enhance reflection picking. Results reveal systematic EM velocity contrasts among host rocks and oxide or sulfide-bearing systems, with oxide-rich and massive sulfide intervals exhibiting higher density, elevated conductivity and susceptibility with strong EM attenuation. The integrated dataset shows that conductivity and magnetic susceptibility significantly influence EM velocity response and detectability limits. The proposed multi-parameter benchmark enables enhanced discrimination of lithological and mineralization controls in mineral exploration workflows and supports more accurate time–depth conversion in HFEM geophysical and ground-penetrating radar (GPR) methods. Full article

Reliable production forecasting is a critical task for evaluating asset valuation and commercial performance in oil and gas reservoirs. Conventional short-term forecasting methods, such as Arps’ decline curve analysis, rely on simple mathematical curve fitting and often oversimplify reservoir performance. On the other hand, long-term forecasting requires complex multidisciplinary models that integrate geophysics, reservoir engineering, and production engineering, but these approaches are time-consuming and have high turnaround times. To bridge the gap between long and short-term production forecasts, reduced-physics models such as Blasingame type curves have been developed, incorporating transient well behaviour derived from diffusivity equations and Darcy’s law. These models assume homogeneity and uniform reservoir properties, enabling faster results while honouring pressure performance. However, despite their efficiency, they still face limitations in reliability, particularly when extended to long-term forecasts. This paper proposes a hybrid modelling approach that integrates flowing material balance (FMB) concepts into physics-informed neural networks (PiNNs) and machine learning models to improve the accuracy and reliability of production forecasting. The proposed methodology introduces two hybrid strategies: physics-informed models enriched with FMB feature, and PiNNs. The first proposed hybrid model uses a created FMB-derived feature as input to neural networks. The second PiNN model embeds data-driven loss functions with a physics-based envelope to reflect reservoir response into the machine learning model. The primary loss function is mean squared error, ensuring minimization of data misfit between predicted and observed production rates. The study validates both proposed physically informed neural network models through performance metrics such as RMSE, MAE, MAPE, and R². Results application on field data shows that the integration of FMB into neural network models using the PiNN concept guides the neural network models to predict the production rates with higher reliability over the full span of the tested data period, which was the last year of unseen production data. Additionally, the proposed PiNN model is able to predict the well productivity index via hyper-tuning of the PiNN model. Furthermore, the PiNN is not improving the metric performance of conventional neural networks, as it has to satisfy an additional material balance equation. This is due to a lower degree of freedom in the PiNN models. Full article

Weather is an essential part of life that affects livelihoods such as agriculture, aviation, etc. Existing systems for weather prediction use deep learning frameworks such as Recurrent Neural Networks and Long Short-term Memory. These models, however, suffer from vanishing gradients that affect the accuracy of the prediction. Using the Deep Belief Networks, we developed a model to address this. Historical weather data is obtained from the Philippine Atmospheric, Geophysical and Astronomical Services Administration for model training. The ground-level sensor data was used to normalize the inputs for the model. The resulting multiclass accuracy is 80%. A larger dataset is recommended for better performance. Full article

Understanding dry-out-induced weakening of reservoir and caprock rocks driven by gas displacement is critical for ensuring the operational safety and efficiency of underground gas storage (UGS). Using core samples from the Xiangguosi UGS collected from different regions and stratigraphic intervals, we quantify the evolution of dynamic elastic parameters during simulated downhole dry-out with a joint ultrasonic and nuclear magnetic resonance (NMR) monitoring system. The results show that as water saturation (S_w) decreases, the dynamic bulk modulus (K_d) and P-wave velocity (V_p) decline by varying degrees across specimens, with reductions ranging from 3.0% to 50.48% and from 1.34% to 17.56%, respectively, whereas the dynamic shear modulus (G_d) and S-wave velocity (V_s) show only minor variations throughout the process. These findings demonstrate that the sensitivity of dynamic parameters to dry-out is strongly specimen-dependent. Further analysis indicates that the dry-out response is highly variable and depends on a combination of petrophysical properties. Among these, the heterogeneity of the initial pore structure acts as an important factor, with its influence shaped by mineralogy and bulk frame rigidity. Cores with multimodal pore size distributions and well-developed macropores (long T₂ components) respond more strongly to dry-out, whereas higher clay mineral contents tend to mitigate modulus degradation by retaining water under stronger capillary confinement. Based on these observations, we propose a conceptual model of pore support and skeleton constraint. The model suggests that dry-out weakening arises from a progressive loss of pore fluid volumetric support to the rock skeleton as free water is preferentially displaced from meso- and macropores. These findings provide key experimental evidence and mechanistic insights for using geophysical methods to monitor dry-out zone expansion and to assess long-term formation stability in UGS. Full article

The craters on the moon hold important clues about the history of impacts in our solar system. To address the limitation of traditional intelligent methods in detecting buried craters, this study proposes a novel intelligent detection approach based on DEM and gravity data. We designed a hybrid network architecture (ResNet + ViT) that combines the local feature extraction strengths of Convolutional Neural Networks with the global context modeling capabilities of Vision Transformer. By combining the complementary information from DEM and gravity anomaly data, it achieves comprehensive detection of lunar craters—from those visible on the surface to buried subsurface structures. To mitigate the inherent sample imbalance in both gravity anomaly and DEM training data, we employ a U-Net architecture augmented with residual blocks and train it using a Focal Loss function with dynamic focusing parameters. Experimental results show that: (1) The proposed method attains high segmentation accuracy, achieving a mean Intersection over Union of 81.3% on the DEM test set and 82.6% on the gravity anomaly test set, respectively. (2) Our method outperforms U-Net and its mainstream variants, achieving a precision of 89.48% and superior detection completeness. (3) Application to representative geological units, including the Wugang Basin, Archimedes Crater, and Mare Moscoviense, validates the robustness and practical utility of our method. This study, thus, provides a novel technical framework for global-scale mapping of lunar impact craters and yields new insights into the evolutionary history of the lunar surface. Full article

This study presents the results of monitoring the endangered gastropod Patella ferruginea within a Marine Protected Area in Sardinia. A detailed map of the species distribution was created, and individual density and population structure were analysed by comparing data collected during monitoring campaigns in 2014, 2018, and 2023. A total of 206, 203, and 109 individuals were recorded in 2014, 2018, and 2023, respectively. In 2014 and 2018, 24 and 26 specimens with a maximum diameter of ≥6 cm were observed, while in 2023, only 11 individuals reached this size, with a single specimen measuring 6.5 cm. Linear density showed an average reduction of approximately 37% in the latest campaign compared to the previous one. The coastline under analysis was divided into five sectors based on the degree of protection and exposure to prevailing winds. The overall decline of approximately 50% in the population indicates a decrease affecting all size classes, although it is more pronounced in the larger size classes. Furthermore, analyses of spatial structure using the Minimum Spanning Tree (MST), Clark and Evans’ R index, and Nearest Neighbour Distance (NND) indicate a dispersed distribution already in 2018, which became more pronounced in 2023. These results indicate that current protection measures within the MPA may not be sufficient to ensure long-term population stability, suggesting that conservation strategies for this species should be assessed on a site-specific basis. Full article

This study applies Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) to investigate surface deformation dynamics in the hyper-arid Eastern Province of Saudi Arabia, with emphasis on quantifying sand dune migration and identifying areas susceptible to wind erosion. Utilizing Sentinel-1 SAR data and the MintPy toolbox, ground deformation was quantified with millimeter-scale precision. Results reveal significant subsidence, up to 15 cm/year in landfills, linked to waste compaction and groundwater depletion. Localized uplift of ~4 cm/year on northern peripheries is directly attributed to aeolian sand accumulation from seasonal Shamal winds, providing quantitative evidence of dune migration. While direct measurement of wind erosion (net deflation) remains challenging due to the dominance of depositional signals and the spatial heterogeneity of erosion processes, areas of potential erosion are inferred from negative displacement patterns outside landfill zones and from coherence characteristics indicative of surface instability. The integration of SBAS-InSAR with GPS and ERA5 wind reanalysis resolves the combined influence of aeolian deposition, hydrogeological changes, and anthropogenic activity, offering insights into both components of aeolian dynamics and a replicable model for sustainable land management in arid environments. Full article

Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m³/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (i_n = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas. Full article

Carbonate mound–shoal complexes, despite their complex pore structures and pronounced heterogeneity, represent one of the most productive reservoir units within carbonate formations. Accurately predicting key physical properties—such as porosity, permeability, and flow zone index—from well log data remains a significant challenge for conventional empirical methods. This study investigates the application of machine learning algorithms for optimizing the prediction of reservoir properties in hill-and-plain carbonate bodies. Six machine learning approaches—Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), Long Short-Term Memory Networks (LSTM), K-Nearest Neighbors (KNN), Random Forests (RF), and Gaussian Process Regression (GPR)—are systematically evaluated and compared. The analysis employed flow zone indices, geological data, and well log curves to classify porosity–permeability types. Seven logging parameters were used as input features: spectral gamma ray (SGR), uranium-free gamma ray (CGR), photoelectric absorption cross-section index (PE), bulk density (RHOB), acoustic travel time (DT), neutron porosity (NPHI), and true resistivity (RT). These features were paired with measured physical property values to train and validate the predictive models. Results demonstrate distinct algorithmic advantages for specific properties. The RF model achieved superior performance in permeability prediction, yielding an R² of 0.6824, whereas the GPR model provided the highest accuracy for porosity estimation, with an R² of 0.7342 and an Accuracy Index (ACI) of 0.9699. Despite these improvements, machine learning models still face limitations in accurately characterizing low-permeability zones within highly heterogeneous hill–terrace reservoirs. To address this challenge, the study integrates geological prior knowledge into the machine learning framework and applies cross-validation techniques to optimize model parameters, thereby providing a practical and robust approach for detailed assessment of mound–hoal carbonate reservoirs. Full article

The scarcity and incompleteness of well-log data pose a critical challenge to deep learning-based reservoir prediction. To address this small-sample problem and improve prediction quality, we propose a novel semi-supervised asymmetric deep co-training framework integrated with a shape context descriptor. This method leverages abundant unlabeled seismic data as well as complementary information on related physical properties. Specifically, we introduce a shape context descriptor to encode seismic waveform morphology and spatial context, thereby improving the lateral continuity and interpretability of predictions while mitigating issues inherent in the sequence-to-point paradigm, wherein three-dimensional seismic data are used as input and a single target point is predicted. To overcome data limitations, a sliding-window resampling strategy is employed to expand the training samples. For co-training, we design an asymmetric dual-task architecture wherein one model performs porosity regression while the other conducts reservoir type classification, thereby enabling synergistic learning. The proposed framework is validated using real three-dimensional seismic data from the Yinggehai Basin in the South China Sea through ablation experiments. The results demonstrate superior performance in prediction accuracy, spatial consistency, and training stability compared to baseline methods. Full article

The Rio Grande Rise (RGR) is the largest oceanic plateau in the South Atlantic and represents a key natural laboratory for understanding oceanic plateau formation, deep-sea circulation, ecosystem functioning, and ferromanganese crust development. This study presents a critical synthesis of current scientific knowledge on the RGR, integrating geological, geophysical, oceanographic, biological, and geochemical evidence published over the last two decades. Geophysical data reveal a complex tectono-magmatic evolution involving Late Cretaceous plume-related volcanism, crustal thickening, rifting, and subsequent subsidence. The structural framework of the plateau is dominated by the Cruzeiro do Sul Rift, which plays a central role in controlling sedimentation, magmatism, and seawater circulation. Oceanographic studies demonstrate that the interaction between the southern branch of the South Equatorial Current and the complex topography of the RGR generates intense internal tides and bottom currents, strongly influencing sediment transport and benthic habitats. Biological investigations indicate that the RGR hosts diverse deep-sea communities, including sponge grounds, cold-water corals, and associated fauna, whose distribution is tightly linked to geomorphology and hydrodynamics. Ferromanganese crusts occurring on the plateau preserve valuable geochemical records of oceanographic and redox conditions, although their spatial distribution, thickness, and metal budgets remain incompletely constrained. Despite major advances, significant knowledge gaps persist regarding crustal structure, sedimentary evolution, ecosystem functioning, and mineral formation processes. This review highlights these uncertainties and outlines research priorities necessary to improve understanding of oceanic plateaus and deep-sea systems in the South Atlantic. Full article

The viae romanae (Roman roads) were constructed according to precise designs and exceptional engineering techniques, ensuring their strength and durability. They represent an immeasurably important factor in human history. Their impact has been universal, facilitating the movement of people, goods, ideas, beliefs and religions over the centuries. The Via Appia Regina Viarum, built between the end of 4th and 1st centuries BCE, connected Rome to Brundisium, spanning the region of Latium and Apulia. The road initially crossed the coastal plains of the Tyrrhenian Sea (in Latium) before cutting through the reliefs and river valleys of the southern Apennines (in Campania) and finally crossing the regio Apulia et Calabria via Tarentum, to the harbor of Brundisium, along the Adriatic coast. In 2024, the Italian Ministry of Culture proposed the ‘Via Appia Regina Viarum’ for inscription on the Unesco World Heritage List, recognizing its unique and exceptional testimony to Roman civilization. Later that same year, the nomination was accepted, and today, the Via Appia is part of the UNESCO World Heritage List. A significant contribution to this nomination came from the multidisciplinary studies and research conducted along the Via Appia between the ancient cities of Beneventum and Aeclanum in the Campanian Apennine, including: (1) geoarcheological investigation aimed at identifying the ancient path of the road, which was not well documented in the area between Beneventum and Aeclanum; (2) studies focused on cultural and geological heritage along the road and its surrounding landscapes, enhancing the value of the nomination; and (3) the organization of social and cultural events designed to disseminate scientific findings and raise awareness among scientists, students, local and national administrators, local food and wine producers, and the general public. This paper highlights the pivotal role of geoscience at all stages of the project: from preliminary field surveys and mapping of landforms and lithofacies, to targeted field and geophysical surveys, to archaeological excavation and geoarchaeological consideration, and to the dissemination of new data through cultural events. Full article

Artificial dams are key retaining structures in underground coal mine reservoirs, and their mechanical performance directly affects the safety and stability of underground water storage systems. This study investigates the effects of dam type, hydraulic pressure, and top boundary condition on dam behavior using three-dimensional finite element models developed in ABAQUS. Three representative dam types, namely flat slab, gravity, and arch dams, were analyzed under three upstream water pressures (0.5, 1.0, and 1.5 MPa) and three top boundary conditions (free, simply supported, and fixed), resulting in 27 numerical cases under an overburden pressure of 4 MPa. The results show that increasing water pressure consistently increases displacement and stress in all dam types, while the deformation mode and stress redistribution strongly depend on structural form and top restraint. The flat slab dam is more prone to edge cracking and local stress concentration, the gravity dam exhibits better overall stiffness and deformation stability, and the arch dam provides more efficient stress redistribution but shows stronger edge effects under restrained conditions. Overall, the gravity and arch dams demonstrate better mechanical adaptability than the flat slab dam. These findings provide a numerical basis for dam-type selection, structural optimization, and local reinforcement design in underground coal mine reservoirs. Full article

Inverse scattering problems seek anomalies in a medium given data measured after the interaction with emitted waves. Due to noise, predictions about the nature of these inclusions should be complemented with uncertainty estimates. To this end, we propose a progressive framework for inverse scattering from low- to high-dimensional Bayesian formulations depending on the prior information and the problem complexity. We aim to reduce computational costs by exploiting educated prior information. When we look for a few well-separated inclusions in a known medium with information about their number, we resort to low-dimensional parameterizations in terms of a few random variables representing their shape and material constants. We test this approach detecting anomalies in tissues and deposits in stratified subsoils. In more complex situations where the anomalies may overlap, we propose high-dimensional parameterizations obtained from Karhunen–Loève (KL) or Fourier expansions of the density and velocity fields. We employ these methods to characterize oil and gas reservoirs in a salt dome configuration, where the screening effect of the dome cap prevents the obtention of adequate prior information. We characterize the posterior probability by means of affine invariant ensemble and functional ensemble MCMC samplers depending on dimensionality. This provides information on configurations with the highest a posteriori probability and the uncertainty around them, identifying factors that could reduce the uncertainty. In high-dimensional setups, techniques based on KL developments are more effective and stable. A recurring issue is the choice of the a priori covariance (which strongly affects the results) and the choice of its hyperparameters. Here, we use educated choices. Formulations that include them as additional parameters could be a next step at a higher cost. Full article