Natural Resources Research最新文献

This study conducted a cyclic loading–unloading (CLU) test on broken coal samples with three particle sizes (0–5 mm, 5–10 mm, and 10–15 mm) under four different stress path conditions. The evolution permeability characteristics of samples during repeated compaction were investigated. The dimensionless permeability and the porosity variation law were obtained under CLU conditions. The permeability loss difference (PID) index was defined, and the permeability damage was analyzed. The permeability evolution model under mining influence conditions was constructed. Results indicate that an increase in maximum loading stress (MLS) exacerbates the seepage network channel destruction of broken coal. As the MLS increases, there is a decrease in permeability recovery rate during the unloading stage and an increase in permeability loss rate during the loading stage. The first stress loading results in a rapid reduction in the porosity, while the subsequent CLU has a minor impact on porosity variation. Results of the PID analysis show positive correlation between the permeability attenuation degree and the MLS. Furthermore, both the permeability recovery rate and the permeability loss rate increase with increase in particle size, indicating that the effects of pressure relief and stress recovery have a greater influence on larger particles. Theoretical permeability values of model were verified with test results, and their high consistency proves the permeability evolution model’s feasibility. The results will help provide theoretical guidance for gas extraction in goaf.

Land suitability analysis is essential for informed farming decisions, revealing an area’s natural potential and limitations. The primary objective of this research is to determine the suitability of land for cultivating major food grain crops like Kharif rice, Rabi rice, and Green gram in the Sundarban region of India using geostatistics, the fuzzy-AHP (FAHP) algorithm, and GIS tools. Local experts’ insights were harnessed to ascertain the relative importance of 19 thematic layers encompassing climatic, soil, environmental, and socioeconomic factors. These were combined using the FAHP model in a GIS to produce a cropland suitability map. The soil parameters were best fitted using spherical and Gaussian semi-variogram models, which showed the best performance. Land suitability analysis revealed that highly suitable (S1) areas were most extensive for Rabi rice (21.65%), followed by those for Kharif rice (16%) and Green gram (11.8%). Moderately suitable (S2) areas dominated the landscape, with those for Kharif rice (68.70%) and Rabi rice (65.32%) exhibiting significantly larger extents than those for Green gram (44.28%). Minor limitations restricted these areas due to low organic content, salt stress, acidic pH, sandy-loamy soil texture, shallow soil depth, and poor-quality irrigation water. Marginally suitable (S3) areas for Kharif rice (14.97%), Rabi rice (12.62%), and Green gram (37.88%) were less extensive, while not suitable (N) areas were minimal (0.33–6.04%). The dependability of the FAHP procedure in suitability assessment was validated using the area under curve (AUC), which was found to be substantial for Kharif rice (81.20%), Rabi rice (83.30%), and Green gram (79.41%). The study concluded that the combined FAHP algorithm in GIS is a practical approach for assessing accurately land suitability for producing specific crops.

Basin modeling and thermal maturity estimation are crucial for understanding sedimentary basin evolution and hydrocarbon potential. Assessing thermal maturity in the oil and gas industry is vital during exploration. With artificial intelligence advancements, more accurate evaluation of hydrocarbon source rocks and efficient thermal maturity estimation are possible. This study employed 1D basin modeling using PetroMod and a novel hybrid group method of data handling (GMDH) neural network optimized by a differential evolution (DE) algorithm to estimate thermal maturity (Tmax) and assess kerogen type in Triassic–Jurassic source rocks of the Mandawa Basin, Tanzania. The GMDH–DE addresses the limitations of conventional methods by offering a data-driven approach that reduces computational time, overcomes overfitting, and improves accuracy. The 1D thermal maturity basin modeling suggests that the Mbuo source rocks reached the gas–oil window in late Triassic times and began expulsion in the early Jurassic while located in an immature-to-mature zone. The GMDH–DE model effectively estimated Tmax with high coefficient of determination (R² = 0.9946), low root mean square error (RMSE = 0.004), and mean absolute error (MAE = 0.006) during training. When tested on unseen data, the GMDH–DE model yielded an R² of 0.9703, RMSE of 0.017, and MAE of 0.025. Moreover, GMDH–DE reduced the computational time by 94% during training and 87% during testing. The results demonstrated the model’s exceptional reliability compared to the benchmark methods such as artificial neural network–particle swarm optimization and principal component analysis coupled with artificial neural network. The GMDH–DE Tmax model offers a unique and independent approach for rapid real-time determination of Tmax values in organic matter, promoting efficient resource assessment in oil and gas exploration.

Rockburst is one of the most hazardous geological disasters in underground engineering due to its complex causes and destructive nature. To address this, there is an imperative for methodologies that can predict rockbursts quickly and effectively to mitigate preemptively the risks and damages. In this study, 259 rockburst instances were analyzed, employing six rockburst feature parameters: maximum tangential stress (σ_θ), uniaxial compressive strength of rock (σ_c), uniaxial tensile strength of rock (σ_t), stress coefficient (σ_θ/σ_t), rock brittleness coefficient (σ_c/σ_t), and elastic energy index (Wet) as inputs. By integrating three novel meta-heuristic algorithms—dingo optimization algorithm (DOA), osprey optimization algorithm (OOA), and rime-ice optimization algorithm (RIME)—with support vector machine (SVM), hybrid models for long-term rockburst trend prediction were constructed. Performance evaluations through fivefold cross-validation revealed that for the no rockbursts, DOA–SVM (Pop = 200) demonstrated superior predictive performance, achieving an accuracy of 0.9808, precision of 0.9231, recall of 1, and an F1-score of 0.96. For moderate rockbursts, OOA–SVM (Pop = 100) emerged as the most effective, registering an accuracy of 0.9808, precision of 0.9545, recall of 1, and an F1-score of 0.9767. For light and severe rockbursts, DOA–SVM, OOA–SVM, and RIME–SVM showcased comparable predictive outcomes. However, these hybrid models outperformed traditional SVM models optimized with conventional algorithms in terms of accuracy across all rockburst hazard levels. Moreover, the hybrid models underwent additional validation with a new dataset of 20 rockburst instances collected globally, confirming their robust efficacy and exceptional generalization capabilities. An ensuing analysis using local interpretable model-agnostic explanations (LIME) on the six key feature parameters revealed a significant positive correlation between σ_θ and Wet with the severity of rockbursts. These results not only affirm the superior optimization performance of the DOA, OOA, and RIME algorithms but also their substantial potential to enhance the predictive accuracy of machine learning models in forecasting long-term rockbursts.

Carbonatites are the primary geological sources for rare earth elements (REEs) and niobium (Nb). This study applies machine learning techniques to generate national-scale prospectivity models and support mineral exploration targeting of Canadian carbonatite-hosted REE +/− Nb deposits. Extreme target feature label imbalance, diverse geological settings hosting these deposits throughout Canada, selecting negative labels, and issues regarding the interpretability of some machine learning models are major challenges impeding data-driven prospectivity modeling of carbonatite-hosted REE +/− Nb deposits. A multi-stage framework, exploiting global hierarchical tessellation model systems, data-space similarity measures, ensemble modeling, and Shapley additive explanations was coupled with convolutional neural networks (CNN) and random forest to meet the objectives of this work. A risk–return analysis was further implemented to assist with model interpretation and visualization. Multiple models were compared in terms of their predictive ability and their capability of reducing the search space for mineral exploration. The best-performing model, derived using a CNN that incorporates public geoscience datasets, exhibits an area under the curve for receiver operating characteristics plot of 0.96 for the testing labels, reducing the search area by 80%, while predicting all known carbonatite-hosted REE +/− Nb occurrences. The framework used in our study allows for an explicit definition of input vectors and provides a clear interpretation of outcomes generated by prospectivity models.

The combination of acoustic emission and electrical potential monitoring methods holds promise for monitoring and warning of rock bursts due to its comprehensive reflection of the damage process. However, the response features during the creep failure process remain unclear. In this paper, a coal creep test was conducted using a combination of electric potential and acoustic emission monitoring. The response characteristics were analyzed, their multifractal characteristics were analyzed, and the joint response mechanism was explored. This research demonstrated a significant correlation among acoustic emission and electrical potential signals and creep deformation and failure. At the start of loading, a brief increase in both signals was observed. As deformation progressed, the signals became steady, and their intensity and fluctuation notably increased during accelerated creep failure. Quantitative analysis of acoustic emission count rates and electric potential intensity during creep processes revealed a quadratic relationship of acoustic emission count rates with stress and strain variations, in addition to an exponential correlation with mean electric potential intensity. Additionally, the statistical analysis of the multifractal characteristics before coal sample instability and failure revealed consistent trends in the characteristic values of Δα and Δf(α), with initial decrease followed by slight fluctuations, culminating in a sudden abnormal change preceding failure. Finally, leveraging the mechanisms of acoustic emission and electrification under load, this study discusses the multifractal characteristics of acoustic-electric signals and verifies their complementary roles in accurately predicting coal rock creep failure. These studies provide essential theoretical groundwork and references for improving dynamic disaster monitoring in coal mines.

The presence of water in coal and its interaction plays pivotal roles in the storage and migration of coalbed methane (CBM), making it imperative to understand the water-occurrence across different coal ranks to guide CBM exploitation effectively. Here, a novel method for categorizing water into condensed and adsorbed forms based on their dehydration dynamics is proposed using differential thermogravimetric curve and the Arrhenius equation, offering a straightforward process and enabling the assessment of the interaction strength between water and coal. The result indicates that the total water capacity decreases initially before subsequently increasing as coal rank increases from 0.28 to 2.33% R_o,max, with the ratio of condensed water exhibiting an S-shaped curve. Remarkably, the condensed water capacity is correlated linearly with the total pore volume. The adsorbed water in low-rank coal is controlled primarily by the level of oxygen functional groups, whereas in medium-high rank coal it is controlled primarily by the specific surface area. Based on this, the controlling equations of water capacity and coal–water structure models were established. Additionally, coal–water interaction strength decreases significantly after the first coalification jump, with the strength of low-rank coal being approximately 2.54 times higher than that of medium-high rank coal. This discrepancy arises from the combined influence of multiple oxygen functional groups in low-rank coal on adsorbed water. This paper enhances the understanding of drainage process in coal reservoirs of varying ranks, which can facilitate the efficient extraction of CBM.

Iron ore has had a highly global market since setting a new pricing mechanism in 2008. With current dollar values, iron ore concentrate for sale price, which was $39 per tonne (62% Fe) in December 2015, reached $218 per tonne (62% Fe) in mid-2021. It is hovering around $120 in October 2023 (cf. https://tradingeconomics.com/commodity/iron-ore). The uncertainty associated with these fluctuations creates hardship for iron ore mine operators and steelmakers in planning mine development and making future sale agreements. Therefore, iron ore price forecasting is of special importance. This paper proposes a cutting-edge multi-echelon tandem learning (METL) model to forecast iron ore prices. This model comprises variational mode decomposition (VMD), multi-head convolutional neural network (MCNN), stacked long short-term-memory (SLSTM) network, and attention mechanism (AT). In the proposed METL (i.e., the combination of VMD, MCNN, SLSTM, AT) model, the VMD decomposes the time series data into sub-sequential modes for better measuring volatility. Then, the MCNN is applied as an encoder to extract spatial features from the decomposed sub-sequential modes. The SLSTM network is adopted as a decoder to extract temporal features. Finally, the AT is employed to capture spatial–temporal features to obtain the complete forecasting process. Extensive computational experiments are conducted based on daily-based and weekly-based iron ore price datasets with different time scales. It was validated that the proposed METL model outperformed its single-echelon and other categorized models by 10–65% in range. The proposed METL model can improve the prediction accuracy of iron ore prices and thus help mining and steelmaking enterprises to determine their sale or purchase strategies.

The Ecca equivalent coal deposits in the Morupule, Mmamabula, and Mabesekwa coalfields exist within the Kalahari Karoo Basin of the Karoo Super Group. Only the Morupule coal has proved its potential for power generation; while, the utilization value of the Mmamabula and Mabesekwa coals is yet to be determined. The current study presents petrographical characteristics of the three seams from each coalfield. Reflected light microscopy combined with scanning electron microscopy was used in the study. The analyses revealed that the Morupule and Mabesekwa coals are rich in inertinite; while, the Mmamabula coal is rich in vitrinite. The vitrinite reflectance indicates that the coal rank stretches between high volatile bituminous B and high volatile bituminous A. The compositions of macerals and coal facies indicate variations in the depositional conditions for the three studied coalfields. The Morupule and Mabesekwa coals were accumulated in a terrestrial bedmont dry forest swamp through fluvial vegetation transportation; while, the Mmamabula coal was deposited in a limnic upper plain wet forest swamp through alluvial vegetation transportation. Hydrological conditions were rheotrophic except in the Mmamabula South, which had ombrotrophic conditions. Based on petrographic compositions, coals from the Mmamabula East, Mmamabula South, Morupule East Main, Morupule West Main and Morupule South would be suitable for carbonization, gasification and liquefaction processes. However, several challenges with coals from the Mmamabula South, Mabesekwa Seam B, Mabesekwa Seam C and Mabesekwa Seam E would be encountered during carbonization, gasification and liquefaction due to high ash content and inert semifusinite content.

Southern Tunisia belongs to the Sahara desert, one of the driest regions of the world, where groundwater research is crucial to satisfy the water demand. In this region, the Jurassic aquifer appears as a potential resource. Nevertheless, the related information is too limited to develop a suitable plan for exploitation. The present study aimed for a thorough understanding of the Jurassic series using borehole and seismic reflection data. Well logs from 40 petroleum boreholes were analyzed both qualitatively and quantitatively to define precisely the potential water reservoirs and determine their petrophysical characteristics. Comparison of the various recordings revealed the abundance of sandstone and dolomite deposits in the Sebaia Formation (Dogger–Malm) and the evaporitic composition of the Abreghs Formation (Lias). The Sebaia Formation is enriched in clays toward the south as indicated by well logs correlation and computed shale volumes (V_sh reaching 27.8%). The south-east part of Southern Tunisia contains mostly sandy Jurassic reservoirs, exhibiting the highest estimated porosities (22.8–31%). Lithostratigraphic correlations were established to firstly approach the geometry of the Jurassic aquifer. These correlations highlighted that the Jurassic series have variable depth and thickness along the Dahar structure, which thicken and deepen from the Dahar to the west and disappear in the Jeffara. These findings were further refined by the interpretation of 198 seismic profiles, which display several NW–SE-, E–W- and NE–SW-trending normal faults that influenced the Jurassic reservoirs depth, thickness, facies and petrophysical characteristics as well as groundwater circulation. The present study yielded interesting results that may enormously guide the investigation of the Jurassic aquifer in Southern Tunisia. Furthermore, it may be considered as an example for hydrogeophysical applications in the “Sahara” and other arid zones worldwide.