Natural Resources Research最新文献

With the rapid development of economy and society, decision-makers need a deep understanding of the role of sustainable ecosystem management in practical application, which is crucial to the sustainable development of ecosystems. Understanding the relationship between ecosystem supply and social demand is key in overcoming the contradiction between humans and the ecosystem, as well as achieving successful ecological restoration and healthy regional development. However, there is still a lack of multi-scale and multi-method research on the supply–demand relationship of ecosystem services for key ecological-restoration zone. In this study, a typical ecological barrier (i.e., Ganzhou City of southern China) was selected as the research area, and five ecosystem services (ESs) including water yield (WY), carbon sequestration (CS), crop production (CP), soil retention (SR) and recreation service (RE) were investigated at the pixel, township and county scale in 2020. The balance and coordination analysis of ESs supply and the quantitative analysis of the supply–demand relationship (using the coupling coordination degree (CD), matching degree (MD), and supply–demand ratio (SDR)) were executed under multiple scales. Finally, based on the MD at the township scale, Ganzhou City was divided into four areas: high-demand, low-demand, low-supply and high-supply areas. The results reveal a trade-off relationship between CP and CS at the pixel scale, with no trade-off relationships observed among the ESs at the other scales. The supply–demand relationships of different districts and counties in Ganzhou City are highly uncoordinated, with distinct spatial mismatching. Areas with high CD values are located in Chongyi County and Longnan County, while the low/value SDR area is located in Zhanggong District, with a relatively high population and GDP. The results of MD division based on township level in Ganzhou City show 17 townships are classified as high-demand areas (mainly in urban areas) and 23 townships are classified as low-demand areas (mainly in the suburbs); 77 townships are classified as low-supply areas and 167 townships are classified as high-supply areas, which are mainly distributed in forest areas. This study provides guide for regional ecological space planning, enhances the understanding of ESs management, and provides a more objective reference for decision makers to plan future ecological restoration.

Coalbed methane (CBM) mining has always been plagued by low mining efficiency. CBM-rich areas have high mining efficiency, but their formation is affected by CBM seepage. Through on-site mining, it is found that gas seepage in low-permeability areas presents nonlinear characteristics. In this paper, the causes and rules of nonlinear seepage are analyzed. The pore structure characteristic parameters such as pore size distribution, pore proportion and fractal dimension were obtained by nuclear magnetic resonance technology, and the pore structure characteristics of low permeability coal are analyzed. The results showed that the proportion of porosity was 6.30–11.02% and the proportion of percolation pores was 7.52–19.59%. Both total porosity and percolation pores had fractal characteristics. Aiming at the nonlinear problem of gas seepage law in low permeability coal, the permeability and gas flow data of coal samples under different gas pressures were measured by the self-designed coal core permeability automatic tester, and the gas seepage characteristics of low permeability coal samples are studied. The experiment showed that there was a starting pressure gradient in gas seepage in coal samples, and the relationship between gas permeability and gas pressure turned over at 1.25 MPa, indicating that there is a slip effect in coal pores. Considering the influence of pore structure parameters on the nonlinear seepage characteristics of CBM, the relationships between porosity, tortuosity, pore proportion, and fractal dimension and Kirschner permeability, slippage factor, and starting pressure gradient were fitted. The analysis showed that the pore size distribution characteristics with large proportion of micro-pores in low permeability coal made the pressure gradient required for the internal gas seepage larger, the influence of the slippage effect was enhanced, and the seepage of gas was nonlinear. To conclude, the influence of micro-pores developed in low permeability coal on nonlinear gas seepage was significant.

Supervised machine learning algorithms are utilized to predict undiscovered mineral resources by analyzing the correlation between geological data and mineral deposits. The scarcity of mineralization and the uncertainty arising from the selection of training samples also the accuracy and generalization of such algorithms. This study employed the adaptive boosting (AdaBoost), gradient boosting decision tree (GBDT), and extreme gradient boosting (XgBoost) algorithms to map the prospectivity of tungsten polymetallic mineral resources in the Nanling metallogenic belt. Firstly, the under-sampling and synthetic minority oversampling technique (SMOTE) methods were used to generate training datasets. Secondly, 50 groups of training datasets were generated using under-sampling, and another 50 groups of training datasets were generated using the SMOTE method. These datasets were used to separately train different boosting algorithms in order to assess the uncertainty associated with the selection of negative samples and the generation of positive samples. Finally, the risk–return analysis was used to mitigate uncertainty, and an enhanced prediction–area (P–A) plot was proposed to evaluate the performance. The results indicate that AdaBoost is the least affected by the selection of negative samples, followed by XgBoost. The SMOTE not only enhances the performance of AdaBoost and XgBoost algorithms but it also reduces the uncertainty related to the selection of negative samples and the generation of positive samples. In addition, the enhanced P–A plot can simultaneously account for both prediction accuracy and uncertainty, making it a potential tool for model evaluation. According to the results, eight potential areas with high return and low risk have been identified as priority areas for exploration. This research not only introduces a new method for mineral prospectivity mapping and uncertainty evaluation but also provides guidance for mineral exploration in this region.

Carbon sequestration in deep, unmineable coal seams is a viable strategy for carbon reduction. However, the impact of CO₂ on coal mechanical performance poses safety concerns for a reservoir. This study proposes an integrated learning methodology that leverages experimental data involving CO₂ immersion in various phases to evaluate the mechanical performance of coal seams during carbon sequestration. The approach integrates support vector regression (SVR) through the bagging method and employs a novel algorithm to optimize SVR. The model systematically assesses seven key factors, including coal rank, sample size, saturation medium, saturation time, saturation pressure, saturation temperature, and loading rate, to understand their influence on mechanical performance. The study identified saturation temperature, coal rank, and the saturated medium as pivotal elements affecting coal seam weakening. Evaluation metrics such as squared correlation coefficient (R²), mean absolute error, and root mean square error were employed for performance comparison between the polynomial model and the integrated model. The results demonstrate the superior performance of the integrated model, with R² of 0.98, emphasizing its effectiveness in predicting coal seam strength weakening during carbon sequestration. These insights contribute to safety assessment of coalbed carbon sequestration practices.

Abstract

The increasing need for indium in photovoltaic technologies is set to exceed available supply. Current estimates suggest only 25% of global solar cell demand for indium can be met, posing a significant challenge for the energy transition. Using the WORLD7 model, this study evaluated the sustainability of indium production and overall market supply. The model considers both mass balance and the dynamic interplay of supply–demand in determining indium prices. It is estimated that a total of 312,000 tons of indium can be extracted. However, the primary hindrance to supply is the availability of extraction opportunities and the necessary infrastructure. Unless we improve production capacity, indium may face shortages, hindering the advancement of pivotal technologies. A concern observed is the insufficient rate of indium recycling. Boosting this could greatly alleviate supply pressures. Projections indicate that indium production will reach its peak between 2025 and 2030, while the peak for photovoltaic solar panels due to indium shortages is anticipated around 2090, with an installed capacity of 1200 GW. Thus, the growth of photovoltaic capacity may lag behind actual demand. For a sustainable future, understanding the role of essential metals like indium is crucial. The European Environment Agency (EEA) introduced four “imaginaries” depicting visions of a sustainable Europe by 2050 (SE2050), each representing a unique future set within specific parameters. Currently, Europe is heavily dependent on imports for tech metals and has limited recycling capabilities, putting it at a disadvantage in a global context. To achieve sustainability, there is a need for improved infrastructure for extraction, recycling, and conservation of metals such as indium. These resources are crucial for realizing Europe’s 2050 sustainability objectives. Furthermore, understanding the role of these metals in wider overarching strategies is vital for envisioning a sustainable European Union by 2050, as depicted in the Imaginaries.

This study investigated the impact of pore accessibility and complexity on gas storage, transport, and recovery potential in the little-studied thermally mature Raghampuram shale samples collected from 2930 to 2987 m depth of Krishna–Godavari basin, India. Our findings reveal that sample nature (powdered, chipped, or cores) and assessment methods significantly influence pore accessibility evaluation, highlighting a research gap in the interpretation of irregularity, complexity, and heterogeneity of shale pore structure using unreliable monofractal theories. Employing a multiscale methodology involving low-pressure N₂ and CO₂ sorption, synchrotron small-angle scattering, and He-pycnometry techniques, we estimated accessibility in powder and core samples. Powder samples displayed a pore accessibility range of 36.07–106.94%, which was a substantial increase (154.54–423.07%) compared to that of solid core samples (1.61–4.16%). Total organic carbon was found to influence closed pore formation, while clay, carbonate, and quartz + K-feldspar contributed to open pores. Multifractal analyses comparing pore heterogeneity and complexity between accessible and inaccessible pores demonstrated higher heterogeneity and complexity in the latter, with accessible pores exhibiting simpler characteristics. Pore size distributions of both accessible and total pores (includes both accessible and inaccessible pores) exhibited multifractal behavior. Our findings emphasize the significance of evaluating pore accessibility and heterogeneity in shale-gas analysis, providing fresh insights into the interlinked elements of pore structure, composition, and gas recovery potential, thus advancing reservoir characterization understanding.

The occurrence of rockburst disasters is closely related to the zonal deformation and failure of rock masses. To effectively monitor and understand the critical zones of surrounding rock damage, it is imperative to establish an extensive network of strain measurement stations. In this study, a comprehensive approach was adopted, integrating acoustic emission (AE), digital image correlation, and fiber Bragg grating demodulation techniques. According to the different deformation stages of the sample, the changes in AE counts and internal strains were analyzed, and the position changes of the AE event location, internal high-strain field, and surface-high-strain field were analyzed, and it was found that there was good spatial correlations among them. Based on the AE events’ counts and localization and internal strain, two new damage variables were proposed, and their numerical values and positions were used to analyze the evolution path of coal sample damage, which was found to match the actual fracture area of the specimen. Therefore, a novel comprehensive monitoring method combining the localization of AE events and internal strain fields is proposed to determine the position of high-damage areas. These research findings are of significant importance for accurately predicting the key areas leading to overall instability in coal–rock masses.

Abstract

The primary goal of mineral prospectivity mapping (MPM) is to narrow the search for mineral resources by producing spatially selective maps. However, in the data-driven domain, MPM products vary depending on the workflow implemented. Although the data science framework is popular to guide the implementation of data-driven MPM tasks, and is intended to create objective and replicable workflows, this does not necessarily mean that maps derived from data science workflows are optimal in a spatial sense. In this study, we explore interactions between key components of a geodata science-based MPM workflow on the geospatial outcome, within the modeling stage by modulating: (1) feature space dimensionality, (2) the choice of machine learning algorithms, and (3) performance metrics that guide hyperparameter tuning. We specifically relate these variations in the data science workflow to the spatial selectivity of resulting maps using uncertainty propagation. Results demonstrate that typical geodata science-based MPM workflows contain substantial local minima, as it is highly probable for an arbitrary combination of workflow choices to produce highly discriminating models. In addition, variable domain metrics, which are key to guide the iterative implementation of the data science framework, exhibit inconsistent relationships with spatial selectivity. We refer to this class of uncertainty as workflow-induced uncertainty. Consequently, we propose that the canonical concept of scientific consensus from the greater experimental science framework should be adhered to, in order to quantify and mitigate against workflow-induced uncertainty as part of data-driven experimentation. Scientific consensus stipulates that the degree of consensus of experimental outcomes is the determinant in the reliability of findings. Indeed, we demonstrate that consensus through purposeful modulations of components of a data-driven MPM workflow is an effective method to understand and quantify workflow-induced uncertainty on MPM products. In other words, enlarging the search space for workflow design and experimenting with workflow components can result in more meaningful reductions in the physical search space for mineral resources.

Hydraulic fracturing causes the fracture zone phenomenon in a coal seam. The effectiveness of coalbed methane (CBM) extraction is determined by the seepage characteristics of various fracture zones under gas pressure attenuation by gas extraction in coal seams. Based on this, this paper developed a zonal seepage test device for hydraulic fracturing coal, designed a series and parallel test method for intact coal sample, microfracture coal sample and penetration fracture coal sample. This method can simulate the migration process of CBM in different disturbance areas near the wellbore in the initial stage of hydraulic fracturing (transverse seepage) and the migration process of CBM from the matrix to the wellbore in the later stage (longitudinal seepage). On this basis, the seepage characteristics of the complex zonal phenomenon produced by hydraulic fracturing under the condition of gas pressure attenuation were studied. A series and parallel permeability model combining Klinkenberg effect, expansion effect and effective stress was established. This model can well describe the series and parallel permeability variation law with gas pressure. It indicated that the adsorption expansion effect and effective stress have an impact on the coal matrix during the longitudinal seepage process, which restricts the seepage of both natural and artificial fractures. Artificial fractures are less impacted by the expansion effect and effective stress during the transverse seepage process, which makes them the primary seepage channel. Combined with the extraction data and the permeability model, the gas production trend in the Qinshui Basin is divided into two stages: transverse seepage dominant stage and longitudinal seepage dominant stage.

Regional geochemical surveys generate large amounts of data that can be used for a number of purposes such as to guide mineral exploration. Modern surveys are typically designed to permit quantification of data uncertainty through data quality metrics by using quality assurance and quality control (QA/QC) methods. However, these metrics, such as data accuracy and precision, are obtained through the data generation phase. Consequently, it is unclear how residual uncertainty in geochemical data can be minimized (denoised). This is a limitation to propagating uncertainty through downstream activities, particularly through complex models, which can result from the usage of artificial intelligence-based methods. This study aims to develop a deep learning-based method to examine and quantify uncertainty contained in geochemical survey data. Specifically, we demonstrate that: (1) autoencoders can reduce or modulate geochemical data uncertainty; (2) a reduction in uncertainty is observable in the spatial domain as a decrease of the nugget; and (3) a clear data reconstruction regime of the autoencoder can be identified that is strongly associated with data denoising, as opposed to the removal of useful events in data, such as meaningful geochemical anomalies. Our method to post-hoc denoising of geochemical data using deep learning is simple, clear and consistent, with the amount of denoising guided by highly interpretable metrics and existing frameworks of scientific data quality. Consequently, variably denoised data, as well as the original data, could be fed into a single downstream workflow (e.g., mapping, general data analysis or mineral prospectivity mapping), and the differences in the outcome can be subsequently quantified to propagate data uncertainty.