Natural Resources Research最新文献_第5页

VoxOct: A Sparse Voxel Framework for Large-Scale Geological Volume Representation VoxOct：用于大规模地质体表示的稀疏体素框架

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-20 DOI: 10.1007/s11053-026-10747-3

Deyun Zhong, Xingliang Xu, Zhaohao Wu, Binglei Li, Lin Bi, Liguan Wang

引用次数: 0

Knowledge-Guided Neuro–Symbolic Modeling for Mineral Prospectivity Mapping: A Case Study of Porphyry Cu–Mo Systems in Boise County, Idaho 知识导向的神经符号建模在矿产勘探制图中的应用：爱达荷州博伊西县斑岩Cu-Mo系统的案例研究

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-17 DOI: 10.1007/s11053-026-10750-8

Weilin Chen, Jiyin Zhang, Chenhao Li, Xiaogang Ma

{"title":"Knowledge-Guided Neuro–Symbolic Modeling for Mineral Prospectivity Mapping: A Case Study of Porphyry Cu–Mo Systems in Boise County, Idaho","authors":"Weilin Chen, Jiyin Zhang, Chenhao Li, Xiaogang Ma","doi":"10.1007/s11053-026-10750-8","DOIUrl":"https://doi.org/10.1007/s11053-026-10750-8","url":null,"abstract":"Mineral prospectivity mapping increasingly relies on machine learning to integrate geochemical and geological information, yet purely data-driven models often perform unreliably under sparse sampling, strong class imbalance, and complex geological controls, while traditional knowledge-driven approaches remain difficult to formalize and scale. This paper presents a neuro–symbolic prospectivity framework that incorporates geological knowledge extracted from deposit-model literature using large language models and embeds it into a data-driven learning pipeline. The framework integrates multi-element geochemical data, spatial geological features, and embedding-based geological priors derived from 87 ore deposit models, where the priors encode semantic relationships between mapped geological environments and mineral system concepts and act as soft guidance rather than deterministic constraints. Model performance is evaluated using spatial cross-validation and area-based targeting metrics that reflect exploration decision-making under limited spatial footprints. A case study of the CUMO porphyry Cu–Mo district (Boise County, Idaho, USA) shows that the proposed framework improves early-stage prospectivity targeting, particularly for joint Cu–Mo anomalies and under strong area constraints, while producing spatial prospectivity patterns that are consistent with mapped lithological and structural controls. The results demonstrate how LLM-assisted neuro–symbolic modeling can bridge geological reasoning and machine learning, supporting interpretable and decision-relevant mineral exploration workflows.","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"25 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148538228","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}

引用次数: 0

Assessing Watershed Health Based on Land-Use Changes and Climate Change Using PSR and Multi-Criteria Decision-Making Methods 基于PSR和多准则决策方法的土地利用变化和气候变化流域健康评估

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-14 DOI: 10.1007/s11053-026-10741-9

Reza Chamani, Sharif Joorabian Shooshtari, Changhyun Jun, Sayed M. Bateni, Mehdi Vafakhah, Fatemeh Akbari Emamzadeh, Halime Joloro

引用次数: 0

A GIS–AHP Framework for Sustainable Well Placement: Application for Natural Gas and Hydrogen Exploration in the Eastern Cordillera Basin, Colombia GIS-AHP框架在可持续井位中的应用：在哥伦比亚东部Cordillera盆地天然气和氢气勘探中的应用

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-11 DOI: 10.1007/s11053-026-10721-z

Salim A. Salon, Sebastian S. Rodriguez, Jhon M. Muñoz-Barrera, Fabián Gallego, Cesar Mora, Deysi P. Forero, Jose F. Osorno, Cristian Peñafort

引用次数: 0

Multi-Scale 3D Convolution Neural Network with Lightweight Attention Mechanisms for Mineral Prospectivity Mapping in Pulang Porphyry Deposit, Yunnan Province, Southwest China 基于轻量级关注机制的多尺度三维卷积神经网络在云南Pulang斑岩矿床找矿中的应用

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-10 DOI: 10.1007/s11053-026-10726-8

Xiaochen Wang, Qinglin Xia, Shuai Leng, Yuqi Liang

引用次数: 0

Improved Salt Dome Imaging Through Mixed $$L_p$$-Norm Regularized Gravity Inversion: Insights from Northwest Iran 通过混合$$L_p$$ -Norm正则化重力反演改进盐丘成像：来自伊朗西北部的见解

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-07 DOI: 10.1007/s11053-026-10729-5

Bardiya Sadraeifar, Reza Ghanati, Rezvan Soltanabadi, Mojtaba Hosseini

引用次数: 0

Characteristics of Coal Spontaneous Combustion and Gas Distribution Under Varying CO2 Concentrations in Goaf Oxidation Zones: Experiments and Simulations 不同CO2浓度下采空区氧化带煤自燃及瓦斯分布特征：实验与模拟

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-06 DOI: 10.1007/s11053-026-10738-4

Huizhu Du, Xiao Cui, Jiayong Zhang, Liwen Guo, Kai Wang, Jianguo Wu, Yanlei Guo, Haijun Guo

引用次数: 0

Assessment of Multiple GRACE Solutions for Estimation of Terrestrial Water Storage Trends over Türkiye 估算<s:1>基耶岛陆地储水趋势的多个GRACE解的评估

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-06 DOI: 10.1007/s11053-026-10746-4

Hilal Şimşek, Emel Zeray Öztürk

引用次数: 0

Bridging the Scale Gap: Regional Refinement of National Mineral Prospectivity Models for Nunavut, Canada 弥合规模差距：加拿大努纳武特地区国家矿产远景模型的区域改进

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-06 DOI: 10.1007/s11053-026-10699-8

Julie E. Bourdeau, Mohammad Parsa, Christopher J. M. Lawley, Steve R. Beyer, Bruce Kjarsgaard, Tommy Tremblay

{"title":"Bridging the Scale Gap: Regional Refinement of National Mineral Prospectivity Models for Nunavut, Canada","authors":"Julie E. Bourdeau, Mohammad Parsa, Christopher J. M. Lawley, Steve R. Beyer, Bruce Kjarsgaard, Tommy Tremblay","doi":"10.1007/s11053-026-10699-8","DOIUrl":"https://doi.org/10.1007/s11053-026-10699-8","url":null,"abstract":"Mineral exploration in the Arctic is challenging, resulting in decreased survey and mineral discovery rates. Data-driven mineral prospectivity mapping is intended to reduce exploration risk by delineating promising areas. However, data sparsity, especially of deposit locations, is a critical bottleneck. In this study, we developed and tested a method to conduct data-driven mineral prospectivity mapping in regions where annotated data are too sparse. Our method is predicated on the existence of large-scale mineral prospectivity frameworks (data, models and methods), which are fine-tuned using regional ground truth to produce locally refined predictions. This is possible because the spatial transferability of trained machine learning models is incomplete. We explicitly tested the hypothesis of limited spatial transferability. To within limitations of our experimental design, our results are supportive of our hypothesis that the incorporation of new positive samples into an existing training dataset preferentially influences the local outcome. Using a suite of critical minerals combined with published data and models in Canada, we demonstrated that by treating large-scale mineral prospectivity frameworks as foundational, it is productive to fine-tune trained models using regional data. Although national-scale maps exist prior to this study, they did not incorporate a substantial amount of ground truth from Nunavut. Therefore, through regional refinement, we produced the inaugural version of a set of pan-Canadian mineral prospectivity maps that are specifically refined for Nunavut for: (1) flake graphite; (2) magmatic Ni (±Cu ±Co ±PGE); and (3) Mississippi Valley-type Pb–Zn. The regional refinement method is a perspectivist and federated approach to MPM that extracts additional value from large-scale mineral prospectivity frameworks and provides an interoperable method for national, regional and inter-regional mineral exploration.","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"12 13 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148409066","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}

引用次数: 0

Improving Sorption Capacity in Tight Reservoirs Through an Advanced Generalized Group Method of Data Handling (g-GMDH) 利用先进的广义群数据处理方法（g-GMDH）提高致密储层吸附能力

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-07-05 DOI: 10.1007/s11053-026-10732-w

Elieneza Nicodemus Abelly, Feng Yang, Nafouant Mouigni Baraka, Irene Martin Ndunguru, Allou Koffi Franck Kouassi, Elizabeth Frederick Mumbuli

引用次数: 0