Natural Resources Research最新文献

{"title":"Integrated Hydrogeophysical Study for the Delineation of Mio–Plio–Quaternary Aquifers in the Central Part of the Sousse Governorate (Tunisian Sahel)","authors":"S. Ben Skander, R. Guellala, W. Abouda","doi":"10.1007/s11053-025-10457-2","DOIUrl":"https://doi.org/10.1007/s11053-025-10457-2","url":null,"abstract":"<p>The Sousse governorate (Tunisian Sahel) is an important socio-economic pole with a strong need for water. Intense drought during the last years has harmed the governorate’s activities. Therefore, groundwater exploitation is increasingly becoming necessary for ensuring sustainable development. It takes in-depth knowledge of aquifers to create an appropriate exploitation plan. In this context, the present study aimed for precise delineation of Mio–Plio–Quaternary aquifers in the central part of the Sousse governorate by thoroughly reconstructing their geometry and understanding their functioning. To reach this goal, a rich database, including 142 water boreholes and 123 vertical electrical soundings (VES), was used. Water borehole data containing lithological columns and their corresponding well logs as well as hydrodynamic and hydrochemical measurements were exploited. Second, VES data were interpreted and geoelectrical cross sections are accordingly established. Two aquifer formations showing variable arrangement were differentiated from well log analysis and correlation: AI and AII, which are Quaternary and Mio–Pliocene in age, respectively. Aquifer AI is absent at the Kalaa Kebira anticline, while on either side of this structure, both formations are present with deepening of aquifer AII. The established piezometric map exhibits groundwater flow toward the north and south of the Kalaa Kebira anticline. In the same directions, water salinity values increase gradually from 1 to 5 g/l. The geoelectrical cross sections highlighted that tectonic deformations control the water reservoirs arrangement and the groundwater circulation. Cross-comparison of the deduced information regarding the aquifers geometry, hydrodynamics, and water quality brings new elements to the hydrogeological scheme in the central part of the Sousse governorate. The Mio–Plio–Quaternary deposits encompass two multilayered aquifers, which are the Balaoum–Sidi Bou Ali aquifer to the north and the Oued Laya aquifer to the south. These aquifers are juxtaposed with a groundwater divide at the Kalaa Kebira anticline. The present study will guide groundwater exploitation in the Sousse governorate and thereby support sustainable development in the Tunisian Sahel. More broadly, it constitutes a model of hydrogeophysical application for better groundwater management in other arid regions.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"59 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143055118","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep Learning-Based Surrogate-Assisted Intelligent Optimization Framework for Reservoir Production Schemes","authors":"Lian Wang, Hehua Wang, Liehui Zhang, Liang Zhang, Rui Deng, Bing Xu, Xing Zhao, Chunxiang Zhou, Li Fan, Xindong Lv, Junda Wu","doi":"10.1007/s11053-025-10458-1","DOIUrl":"https://doi.org/10.1007/s11053-025-10458-1","url":null,"abstract":"<p>Determination of reservoir production schemes has always been a difficult problem during the close-loop management of waterflooding reservoir. Different well control results in significant influence on production, water breakthrough time and recovery rate of producing wells, especially in heterogeneous reservoirs. To optimize well controls, a new method using transpose convolution neural network (TCNN) surrogate model and adaptive differential evolution with optional external archive (JADE) algorithm was introduced. In this method, the TCNN surrogate model, which uses image processing, took well controls (i.e., bottom hole pressure and injection rate) and production time as parameters to predict oil saturation and pressure distribution fields at different time periods. It could well replace a numerical simulator, accurately predict the regional production dynamics at different production time steps, and significantly reduce the simulation time during the optimization process. Meanwhile, the JADE algorithm, as an improved differential evolution algorithm, greatly improved the convergence rate while ensuring the search breadth and it was suitable for solving multi-parameter well control optimization problems. Using a comprehensive reservoir optimization problem as an example, the selection and setting of some parameters during the TCNN training and JADE optimization are discussed. Finally, the method was applied to a real 3D reservoir. The computational speed of the TCNN model was about 3600 times and 2300 times faster than that of a numerical simulation model for the synthetic reservoir and L43 block, respectively.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"34 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143031013","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Lithology Classification Using an Interpretable SHAP Model Integrating Semi-Supervised Contrastive Learning and Transformer with Well Logging Data","authors":"Youzhuang Sun, Shanchen Pang, Hengxiao Li, Sibo Qiao, Yongan Zhang","doi":"10.1007/s11053-024-10452-z","DOIUrl":"https://doi.org/10.1007/s11053-024-10452-z","url":null,"abstract":"<p>In petroleum and natural gas exploration, lithology identification—analyzing rock types beneath the Earth’s surface—is crucial for assessing hydrocarbon reservoirs and optimizing drilling strategies. Traditionally, this process relies on logging data such as gamma rays and resistivity, which often require manual interpretation, making it labor-intensive and prone to errors. To address these challenges, we propose a novel machine learning framework—contrastive learning-transformer—leveraging self-attention mechanisms to enhance the accuracy of lithology identification. Our method first extracts unlabeled samples from logging data while obtaining labeled core sample data. Through self-supervised contrastive learning and a transformer backbone network, we optimize performance using techniques like batch normalization. After pretraining, the model is fine-tuned with a limited number of labeled samples to improve accuracy and significantly reduce reliance on large labeled datasets, thereby lowering the costs associated with drilling core annotations. Additionally, our research incorporates shapley additive explanations (SHAP) technology to enhance the transparency of the model’s decision-making process, facilitating the analysis of the contribution of each feature to lithology predictions. The model also learns time-reversal invariance by reversing sequential data, ensuring reliable identification even with variations in data sequences. Experimental results demonstrate that our transformer model, combined with semi-supervised contrastive learning, significantly outperforms traditional methods, achieving more precise lithology identification, especially in complex geological environments.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"60 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142987613","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Framework for Optimizing the Prediction of Areas Favorable to Porphyry-Cu Mineralization: Combination of Ant Colony and Grid Search Optimization Algorithms with Support Vector Machines","authors":"Sarina Akbari, Hamidreza Ramazi, Reza Ghezelbash","doi":"10.1007/s11053-024-10431-4","DOIUrl":"https://doi.org/10.1007/s11053-024-10431-4","url":null,"abstract":"<p>In the realm of mineral prospectivity mapping, a novel hybrid approach for optimizing hyperparameters of the support vector machine (SVM) algorithm is proposed here. The concept of ant colony optimization (ACO) algorithm, inspired by collective intelligence of ant colonies, and grid search (GS) that systematically evaluate all hyperparameter combinations to find the optimal model configuration are leveraged to fine-tune SVM parameters, enhancing its predictive capabilities. A dataset comprising geophysical, geochemical, geological, tectonic, and remote sensing evidence layers from the Sardouyeh region in Kerman province, Iran, is utilized for model development aimed the prediction of areas favorable for porphyry-Cu mineralization. After generating the regular and tuned predictive models, a comparison was carried out using quantitative performance metrics such as confusion matrix and success rate curves. The results demonstrated that the optimized versions of SVM using ACO (ACO–SVM) and GS (GS–SVM) models exhibit superior performance, achieving better accuracy and predictive capability in identifying locations favorable for porphyry-Cu mineralization. The study highlights the potential of incorporating optimization algorithms, especially ACO, into SVM, leading to the development of more effective predictive models for mineral prospectivity mapping.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"9 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142961771","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Small-Sample InSAR Time-Series Data Prediction Method Based on Generative Models","authors":"Yuchen Han, Xuexiang Yu, Jiajia Yuan, Mingfei Zhu, Shicheng Xie","doi":"10.1007/s11053-024-10434-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10434-1","url":null,"abstract":"<p>In surface deformation monitoring for mining areas, interferometric synthetic aperture radar (InSAR) technology has become a popular research topic due to its efficiency and high accuracy. However, transforming temporal monitoring data into surface deformation predictions remains challenging. In practical applications, InSAR data often face limitations like low acquisition frequency and insufficient data volume, leading to prediction models being prone to overfitting and having poor accuracy. Therefore, this paper proposes an improved temporal convolutional network (TCN) time-series generative adversarial network (GAN) with an attention mechanism, called the Attention–TCN–TimeGAN, to enhance InSAR surface deformation data for better prediction results. By combining the embedding, recovery, generator, and discriminator networks, we used the TCN to expand the receptive field and capture long-term temporal features. Additionally, we integrated the self-attention mechanism into the generator and discriminator to adapt to random vectors, achieving better data generation results. The loss function uses the Wasserstein distance to measure the original data distribution and adds a gradient penalty term with adaptive weights to achieve effective feature extraction from time-series data. Experimental results show that the data generated by our model more comprehensively cover the original data distribution. The prediction results at four test points showed the lowest mean absolute error and mean-squared error and the highest coefficient of determination (R²). These results demonstrate the effectiveness of our generative model in predicting small-sample InSAR time-series data, providing a new method for surface deformation monitoring.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"99 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142936089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Dynamic Evolution of Shallow and Deep Coal Nanopore Structures Under Acidic Fracturing Fluids Using Synchrotron Radiation Small-Angle X-Ray Scattering","authors":"Yingfeng Sun, Shuaipeng Zhu, Hui Wang, Yixin Zhao, Fei Xie, Ping Chen, Changjiang Ji, Zhaoying Chen, Qifei Wang","doi":"10.1007/s11053-024-10449-8","DOIUrl":"https://doi.org/10.1007/s11053-024-10449-8","url":null,"abstract":"<p>Acid fracturing technology is one of the most effective methods for resolving mineral plugging and for improving the pore structure of coal reservoirs. To investigate the characteristics of shallow and deep coal nanopore structures under the influence of acidic fracturing fluids, experiments using synchrotron radiation small-angle X-ray scattering were conducted on shallow and deep coal samples soaked in acidic fracturing fluids of different concentrations for varying durations. This quantitatively characterized the different nanoscale pore scattering intensity ratios (<i>A</i>_<i>I</i>), fractal dimensions, and nanopore parameters. The research indicates that, under the influence of acidic fracturing fluids, the shallow coal nanopore structure tends to become more complex while that of deep coal becomes simpler. The impact of 20% acidic fracturing fluid is greatest on shallow coal nanopore structure, while deep coal nanopore structure is more susceptible to 12% acidic fracturing fluid, with these effects primarily concentrated in the 2–10 nm pores. Acidic fracturing fluids primarily affect the shallow and deep coal nanopore structures by dissolving, among others, carbonate minerals, pyrite, and clay minerals, resulting in the dynamic evolution of the shallow and deep coal nanopore structures during the soaking process.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"98 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142934905","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Approach for Enhancing Geologically Aligned Fusion of Multiple Geophysical Inverse Models in the Porphyry-Cu Deposit of Zaftak, Kerman, Iran","authors":"Morteza Erfanian-Norouzzadeh, Nader Fathianpour","doi":"10.1007/s11053-024-10447-w","DOIUrl":"https://doi.org/10.1007/s11053-024-10447-w","url":null,"abstract":"<p>The simultaneous interpretation of multiple geophysical data through their inverted models of various physical properties of subsurface geological structures and formations related to mineral deposits is a challenging task in mineral exploration. In this paper, a three-dimensional fusion algorithm based on the use of a two-dimensional contourlet transform for concurrent interpretation of multiple geophysical models is proposed. To achieve this, initially, a synthetic model based on a general structure simulating the spatial distribution of physical and geological properties of typical porphyry-Cu deposits using a mineral exploration database is generated, and the results of applying the proposed algorithm to this model are presented. Subsequently, the proposed algorithm is implemented on the Zaftak porphyry-Cu deposit in the southern part of Kerman Province in southern Iran. For this purpose, two fusion models with different contourlet decomposition levels are compared through their consistency with the geological settings of the study area to select the best fusion model using two well-known consistency analyses known as Jensen–Shannon divergence index and BLOB Analysis score. Moreover, the fusion models with 2 and 3 contourlet decomposition levels are compared based on available exploratory data. Finally, based on the validation and conformity of the fused model with the available exploratory borehole data and the geology of the study area, a suitable match for the three-dimensional fused model using two-dimensional contourlet transform with the Jensen–Shannon divergence index of 95.13% and a BLOB Analysis score of 4.68 was found.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917159","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Multifractal Method for Geochemical Element Distribution Analysis","authors":"Mengyu Zhao, Yi Jin, Jiabin Dong, Junling Zheng, Qinglin Xia","doi":"10.1007/s11053-024-10450-1","DOIUrl":"https://doi.org/10.1007/s11053-024-10450-1","url":null,"abstract":"<p>Accurately analysis of the multifractal characteristics of geochemical element distribution is crucial for identifying geochemical anomalies and meaningful element associations. However, the most commonly used multifractal method, i.e., the method of moments, may generate different multifractal spectra for a single element distribution due to variations in the range of moment orders. This is because multifractals and their control mechanisms are not well defined. Fractal topography provides a basis for defining multifractals and clarifies the physical meaning of the singularity index. Therefore, a multifractal analysis method based on fractal topography is proposed to generate a unified multifractal spectrum and give new insight into the singularity analysis of element distribution. The similarities and distinctions between the two methods were evaluated using the de Wijs model. The distributions of two multifractal spectra are shown to be fundamentally consistent. The novel method, nevertheless, utilizes fewer statistics and presents a simplified criterion for element enrichment or depletion. To demonstrate its application, Cu geochemical distribution in the Zhongdian area, China, was used as a case study. Based on the comparison results of the two approaches, the proposed novel approach proves beneficial for accurately characterizing the heterogeneity of geochemical element distribution while maintaining a consistent range of the singularity index. The singularity index distribution map at a fine scale provides a comprehensively detailed zonation of geochemical anomalies and, at different scales, it can effectively reveal and interpret the variation of element distribution.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"4 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142917160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identifying Types and Key Features of Typical Production Performance of Coalbed Methane with Interpretable Residual Graph Convolutional Model","authors":"Yuqian Hu, Yuhua Chen, Jinhui Luo, Mingfei Xu, Heping Yan, Yunhao Cui, Chao Xu","doi":"10.1007/s11053-024-10448-9","DOIUrl":"https://doi.org/10.1007/s11053-024-10448-9","url":null,"abstract":"<p>The production of coalbed methane (CBM) wells is positively correlated with their production performance, and key features of typical production performance can be applied to determine the high production exploration targets. However, accurately classifying the production types of CBM wells and rationally identifying the key controlling factors among them are challenging due to the strong heterogeneity of CBM reservoirs. The data-driven “black-box” algorithms utilized in previous studies often suffer from limited interpretability due to a lack of sufficient domain theoretical foundation. This paper proposes an interpretable residual graph convolutional neural network model (I–RGCN) for classifying the production types and for identifying key features of typical production of CBM wells from spatial relationships and attribute data. This model constructs a topological graph structure based on the spatial correlations among wells and utilizes the dynamic time warping algorithm to assess the similarity of geological feature parameters among CBM wells, incorporating these as edge weights in the model for accurate classification of CBM production types. Subsequently, the GNNExplainer was used to rank the importance of features during the model's decision-making process. Final experiments conducted on datasets from the Fanzhuang–Zhengzhuang block within the Qinshui coalfield demonstrated that the I–RGCN achieves accuracy of &gt; 84% and F1 score of ~ 65%, and outperformed other baseline models and enhanced the interpretability of the results obtained. Thus, this paper offers a novel and interpretable research methodology for the classification of CBM production types and the identification of key features of the production performance of CBM.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"79 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142887050","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Thermal Damage and Acoustic Emission Characteristics of High-Temperature Granite under Liquid Nitrogen Cooling","authors":"Yilei Yuan, Kun Zheng, Chaolin Wang, Yu Zhao, Jing Bi","doi":"10.1007/s11053-024-10446-x","DOIUrl":"https://doi.org/10.1007/s11053-024-10446-x","url":null,"abstract":"<p>Liquid nitrogen fracturing is an efficient stimulation technique for exploiting hot dry rock geothermal energy. Understanding the physical and mechanical damage characteristics of high-temperature reservoir rocks under liquid nitrogen cooling is crucial for the application of liquid nitrogen fracturing technology. Therefore, nuclear magnetic resonance technology, acoustic wave velocity measurement technique, acoustic emission (AE) technology, and 3D scanning technology were used to explore changes in the physical and mechanical properties of high-temperature granite under liquid nitrogen cooling from macroscopic and microscopic perspectives. Our research findings show that, as treatment temperature increased, the internal pore structure of the sample changed gradually, with decrease in proportion of micropores and increase in proportion of macropores. The number of pores of various sizes increased gradually. In particular, after treating the granite to a treatment of 600℃, there was a significant increase in the quantity of pores within the granite, primarily manifested by an increase in macropores. From 25 to 600℃, the compressive strength decreased from 160.79 to 68.44 MPa, a reduction of 57.44%; the tensile strength decreased from 11.13 to 6.02 MPa, a reduction of 45.91%. The fractal dimension of the fracture surface of Brazilian disk samples was calculated using the box-counting method, and the results indicated that an increase in treatment temperature would lead to an increase in roughness of the sample’s fracture surface. During the uniaxial compression tests, the AE parameter rise angle (<i>RA</i>) suddenly increased near the peak load. The straight line relationship (average frequency = 11RA + 60) was used to classify the AE signals generated during uniaxial compression of samples. With increase in treatment temperature, the shear signal increased gradually, which is highly consistent with the macroscopic failure characteristics of the samples.</p>","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"24 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142886997","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}