Natural Resources Research最新文献

Remote sensing data prove to be an effective resource for constructing a data-driven predictive model of mineral prospectivity. Nonetheless, existing deep learning models predominantly rely on neural networks that necessitate a substantial number of samples, posing a challenge during the early stages of exploration. In order to predict mineral prospectivity using remotely sensed data, this study introduced deep forest (DF), a non-neural network deep learning model. Mainly based on ASTER multispectral imagery supplemented by Sentinel-2 and geological data, gold ore in Hamissana area, NE Sudan was used to test the DF predictive model capability. In addition to four geological-based evidential layers, 20 remote sensing-based evidential layers were generated using remote sensing enhancing techniques, forming the predictor variables of the proposed model. The applicability of the DF was thoroughly examined including its accuracy for delineating prospective areas, sensitivity to amount of training samples, and adjustment of hyperparameters. The results demonstrate that DF model outperformed conventional machine learning models (i.e., support vector machine, artificial neural network, and random forest) with AUC of 0.964 and classification accuracy of 93.3%. Moreover, the sensitivity analysis demonstrated that the DF model can be trained with a limited number (i.e., < 15) of mineral occurrences. Therefore, the DF algorithm has great potential and proves to be a viable solution for data-driven prospectivity mapping, particularly in scenarios with data availability constraints.

Western Saudi Arabia is a promising area for geothermal energy exploration. Its geothermal wealth is attributed to the ongoing Red Sea rift evolution and crust thinning. Several hot springs in the region indicate the presence of potential geothermal resources. The present study aimed to characterize the geothermal system of a hot spring in the region, in the area of Wadi Al Lith, where water temperature exceeds 80 °C at the surface. For this, we used aeromagnetic data from the Saudi Geological Survey. We also collected a ground magnetic gradient data profile near the hot spring. To delineate structures of interest and map the distribution of volcanic rocks and tectonic lineaments, data enhancement filters were applied to the aeromagnetic data. These data were also subjected to spectral analysis to determine the depth of the Curie isotherm, which was then used to estimate a 1D geothermal model and predict the heat flow in the study area. According to the results of the spectral analysis of aeromagnetic data, the depth of the Curie temperature isotherm was about 14.8 km. The estimated depth was validated by deep magnetotelluric soundings, which revealed a clear decrease in resistivity at the same depth level. A constrained 1D geothermal model with three different layers (upper crust, lower crust, and mantle) was constructed. The depth of the Curie isotherm and the depth to the lithosphere's base were among the constraints. Furthermore, published data were used to define the radiogenic heat production within the crust and mantle and the corresponding thermal conductivity and thickness of each layer. According to the 1D geothermal modeling results, the average heat flow of the area reaches 109.8 mW/m², indicating potential geothermal resources. The findings of this study can be used to design a drilling program that will provide detailed information on reservoir parameters and put the geothermal resources into production.

The Minle manganese (Mn) deposit is a typical Mn-bearing deposit in the Datangpo Formation in southern China. The metallogenic environment and associated changing processes directly determine the migration, enrichment, and precipitation of Mn. To have a better understanding of the metallogenic structure, magnetotelluric (MT) method was performed to explore the Minle deposit. Electrical spindle analysis of MT data was conducted based on the Swift decomposition and the phase tensor decomposition, and inversion of the transverse-electric (TE) and transverse-magnetic (TM) models was carried out using the Occam inversion method. The results revealed that the main structural strike of the MT section was approximately 37° north to east and obtained the distribution characteristics of the deep electrical properties in the study area. The “concave structure” in the resistivity model is the main geophysical marker for delineating the Mn-ore body. In the metallogenic structure of Mn ore, a “funnel-shaped structure” of the strata was found, which provided favorable space for the percolation and enrichment of Mn deposits. The results of this study will be helpful in improving geophysical prospecting techniques for sedimentary Mn deposits in southern China.

Coal-and-gas outbursts represent a significant hazard in coal mining, with gas expansion energy (GEE) in coal seams being a primary energy source. Accurate GEE assessment is vital for outburst prediction and mitigation, thereby enhancing mining safety. Traditional calculation models have struggled with limited understanding of outburst mechanisms and experimental constraints, leading to broad GEE estimates with considerable discrepancies. Addressing this gap, this study introduces an experiment-driven, highly practical calculation model, along with innovative experimental methods to measure accurately key determinants of GEE: fracture porosity, CH₄ desorption amount, and gas pressure in coal seams. For the first time, this study employed remade and raw coal columns as media to simulate accurately the real conditions of tectonic and raw coal seams for exploring the coupling effects of stress and gas pressure on GEE. This study calculated the GEE as stress increases from 5 to 50 MPa and gas pressure decreases from 2 to 0.5 MPa. The results indicate that, for two remade coal columns, the GEE decreased from 1870 to 62 kJ/t and from 2039 to 356 kJ/t while for the raw coal column, the GEE dropped from 130 to 6 kJ/t.

To investigate the effect of moisture content on coal in cryogenic treatment, based on a gas-containing coal cryogenic treatment simulation testing system and an automated mercury intrusion porosimeter, temperature changes, strains, and characteristic parameters of the pore structure of coal after cryogenic treatment were determined. In addition, a thermal–water–mechanical coupling theoretical model was established using COMSOL software to simulate the changes in temperature and volume of coal. It was observed that moisture content was correlated negatively with the rate of temperature drop of coal and correlated positively with the frost heave strain. After cryogenic treatment, the final volume of coal decreased and the pores increased. The experiment revealed that frost heave heat extended the temperature stabilization time by an average of 27%, while methane adsorption heat had almost no effect. It is recommended to control the moisture content of coal at around 5% when using cryogenic treatment for anti-outburst, while for frozen coring, the moisture content should be controlled below 3%. The research results provide significant understanding of changes caused by cryogenic treatment on coal and supply practical information for optimizing the industrial production application of cryogenic treatment on coal.

The stress-induced evolution of coal fractures significantly affects permeability and, consequently, gas extraction efficiency. This study introduces a novel coal fracture evolution model based on assumptions of fracture morphology and log-normal distribution of fracture aspect ratio. This model offers a theoretical framework for understanding the fracture closure process, ultimately depicting fracture evolution as a combined result of elastic compression and closure. It predicts the decay curve of fracture porosity under hydrostatic pressure loading. We conducted uniaxial compression experiments for determining the mechanical parameters of the model and in situ CT experiments with confining pressure ranging from 0 to 25 MPa for validating the model. The findings indicate the following: (1) Initially, the decline in fracture porosity with stress is predominantly due to elastic compression, followed by a rapid transition to closure. (2) Sensitivity analysis reveals that an increase in two physical quantities—the cube root of the product of the peak aspect ratio and the square of the mean aspect ratio (x_c) and the bulk modulus of the coal matrix (K_m)—results in a decrease in the rate of fracture porosity decay with stress. (3) Tectonic action has a dual effect of augmenting x_c and diminishing K_m. We define the magnification of x_c and the divisor of K_m under a common term—scaling factor. When the scaling factor of x_c is less than that of K_m, the tectonic action promotes the decay of porosity with stress. Conversely, when the scaling factor of x_c is greater than that of K_m, the effect is reversed.

The ore and waste materials extracted from a mineral deposit during the mining process can have significant variations in their physical and chemical characteristics. The current approaches to geological material characterization are often subjective and usually involve a significant human workload, as there is no optimized, well-defined, and robust methodology to perform this task. This paper proposes a robust, data-driven workflow for geological material characterization. The methodology involves selecting relevant features as a starting point to discriminate between material types. The workflow then employs a robust, state-of-the-art nonlinear dimension reduction (DR) algorithm when the dataset is multidimensional to obtain a two-dimensional embedding. From this two-dimensional embedding, a kernel density estimation (KDE) function is derived. Subsequently, a new clustering algorithm, named ClusterDC, is employed to generate clusters from the KDE function, accurately reflecting geological material types while achieving scalable clustering performance on large drillhole datasets. ClusterDC is a density-based clustering algorithm capable of delineating and ranking high-density zones corresponding to clusters of data samples from a two-dimensional KDE function. The algorithm reduces subjectivity by automatically determining optimal cluster numbers and minimizing reliance on hyperparameters. It also offers hierarchical and flexible clustering, allowing users to group or split clusters, optimally reassign data samples, and identify cluster core points as well as potential outliers. Two case studies were carried out to test the algorithm and demonstrate its application to geochemical drill-core assay data. The results of these case studies demonstrate that the application of ClusterDC in the presented workflow supports the characterization of geological material types based on multi-element geochemistry and thus has the potential to help mining companies optimize downstream processes and mitigate technical risks by improving their understanding of their orebodies.

Understanding source rocks' organic content and thermal maturity is crucial in assessing their hydrocarbon potential. To address this, our study focused on developing an accurate artificial neural network (ANN) model for estimating total organic carbon (TOC) content and a complete set of pyrolysis parameters from conventional well logs. The accuracy of the ANN-based technique in estimating TOC content was found to be significantly higher (correlation coefficient of 0.95) compared to the results obtained using Passey's method (correlation coefficient of 0.44). Additionally, the ANN model provided highly accurate predictions for the pyrolysis parameters S1, S2, S3, and Tmax, with correlation coefficients of 0.85, 0.90, 0.86, and 0.93, respectively. The study focused on the Abu Gabra Formation in the Hamra field, and the ANN data analysis revealed that the source rock in this area is of fair to good quality. The assessment of kerogen type indicated a mixed kerogen type II and type III, suggesting the potentiality for oil and gas generation. The predicted parameters further confirmed that the Abu Gabra source rock is thermally mature and capable of generating indigenous hydrocarbons. The results of the ANN-based modeling were consistent with laboratory measurements, demonstrating the reliability of the predictions for comprehensive source rock evaluation using well logs.

In deep mineral exploration, it is difficult to constrain the complex geological structures using a single geophysical method. To tackle the difficulty, integrated geophysical surveys and joint data interpretation are essential. Machine learning (ML) provides more accurate predictions than traditional methods, especially when dealing with complex data from multiple sources or varied statistical distributions. However, a major challenge in using ML for deep mineral exploration is the scarcity and imbalance of labeled samples, mainly due to budget constraints and the complexity of ore deposits. This issue reduces the accuracy of predictive models and introduces bias. Additionally, limited labeling can lead to difficulties in predicting previously undefined classes in training datasets. To address these challenges, we introduce a robust semisupervised ML framework that integrates diverse geophysical and geological datasets to improve model reliability with limited labeled data. Our approach uses a semisupervised ML variational Gaussian mixture model (SsL-VGMM) to handle issues related to insufficient and imbalanced data. We enhanced the model’s predictive capability for unseen data by introducing a novel penalty factor in the ‘cannot-link’ function. Moreover, we employed Bayesian optimization, focusing on the mean-mixture weight, to avoid local optima during model training. Our model demonstrated high accuracy and efficiency, with classification and prediction accuracies of 95.33% and 87.4%, respectively, in numerical and electromagnetic simulation scenarios. Its effectiveness was further validated by locating Pb–Zn–Ag deposits in Inner Mongolia, supported by actual drilling data. This paper highlights the model’s potential in complex mineral exploration and its significant practical and innovative value for deep mineral exploration.

As a crucial factor that influences the hydraulic fracturing effectiveness of coal seams, fracturing fluids have garnered increasing attention. Among them, acid fracturing fluids have demonstrated positive impact on the pore transformation of coal, but high-strength acid fracturing fluids can cause corrosion damage to mechanical equipment. In this study, we employed acetic acid to formulate four types of weak acid fracturing fluids with varying concentrations and conducted soaking experiments. We analyzed the changes in the physical and chemical structure of coal samples before and after treatment using Fourier-transform infrared spectroscopy and X-ray diffraction analysis. The changes in the pore structure of coal samples before and after treatment were characterized by nitrogen adsorption and scanning electron microscopy. Our findings indicate the following: (1) The effects of different concentrations of acetic acid fracturing fluid on functional groups and microcrystalline structure vary. The 5% concentration fracturing fluid had the most significant impact on the organic structure of coal samples, with decreases in the area of hydroxyl structure, aliphatic structure, and oxygen-containing structure of 2.97%, 1.37%, and 0.68%, respectively. The 6% concentration fracturing fluid had the most significant impact on crystal structure, leading to a high degree of recrystallization and a fragile crystal network structure. (2) Fracturing fluids with concentrations below 7% can increase the number of mesopores and simplify the pore structure, while concentrations above 7% can lead to an increase in micropores and a more complex pore structure. (3) After the action of fracturing fluid, carbonate minerals are dissolved, and the pores of coal samples increase. However, excessively high concentrations of acetic acid fracturing fluid can facilitate shedding of mineral particles and block some pore channels, worsening the connectivity between pores. (4) The octadecylamine acetate formed by the combination of octadecylamine and acetic acid develops as a partial film on the surface of a coal body, reducing the roughness of the fracture surface and facilitating the flow of the fracturing fluid. Our findings provide theoretical support for the preparation and selection of weak acid fracturing fluids.