Natural Resources Research最新文献

Uncertainty-Aware Mineral Prospectivity Mapping with Positive-Unlabeled Learning and Conformal Calibration: Ni Mineralization in the Yilgarn Craton 基于正无标记学习和保角校正的不确定性感知矿产找矿性填图：伊尔加恩克拉通镍矿化

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-13 DOI: 10.1007/s11053-026-10778-w

Seongin Ahn,Honggeun Jo,Jihoe Kwon,Gyesoon Park,Sang-ho Lee

{"title":"Uncertainty-Aware Mineral Prospectivity Mapping with Positive-Unlabeled Learning and Conformal Calibration: Ni Mineralization in the Yilgarn Craton","authors":"Seongin Ahn,Honggeun Jo,Jihoe Kwon,Gyesoon Park,Sang-ho Lee","doi":"10.1007/s11053-026-10778-w","DOIUrl":"https://doi.org/10.1007/s11053-026-10778-w","url":null,"abstract":"Abstract Mineral prospectivity mapping (MPM) supports exploration targeting, yet most machine learning approaches emphasize point probabilities with limited uncertainty quantification or coverage guarantees. Regional MPM faces incomplete deposit labels from biased exploration, spatial autocorrelation invalidating random cross-validation, and limited decision-relevant guidance linking probability surfaces to exploration priorities. This study develops a framework that combines positive-unlabeled learning, nested spatial block cross-validation, bootstrap ensemble uncertainty, and cross-conformal calibration, and converts the resulting signals into five priority zones defined by lift-based percentile thresholds and a 30% relative interquartile range ( $$rho _{text {IQR}}$$ ρ IQR ) cut-off. On the Yilgarn Craton, Western Australia (70,200 grid cells at 5 km resolution, 818 cells containing at least one known Ni deposit location, a 1.17% positive rate), the framework uses nested fivefold spatial block cross-validation with 50 km blocks informed by an empirical variogram. XGBoost and a BaggingPU-XGBoost variant achieve nearly identical outer test PR-AUC of 0.195 and 0.194 and ROC-AUC of 0.911, and the Excluded zone removes 9% of the positive cells at empirical coverage exceeding the 85% nominal target. BaggingPU-XGBoost produces more stable high-probability predictions, with median $$rho _{text {IQR}}$$ ρ IQR of 18% in the top 1% of bootstrap mean probabilities compared with 48% for XGBoost. This stability advantage shifts deposits into a more populated Immediate zone, so the combined Immediate and Priority zones of BaggingPU-XGBoost capture 33% of the positive cells compared with 23% for XGBoost. The framework provides an uncertainty-informed prioritization for MPM workflows in which discovery efficiency must be balanced against costs and investment risks.","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"63 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149209659","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

Mirror-Inverted Fault Control Limits Cross-Cluster Transfer of Scalar Gravity–Magnetic Predictors in Mineral Prospectivity Mapping: Evidence from the Sitaihaiquan Pb–Zn Deposit, Western Tianshan, NW China 逆断层控制限制了标度重磁预测在找矿填图中的跨簇传递——以西天山四太海泉铅锌矿为例

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-10 DOI: 10.1007/s11053-026-10783-z

Fuyuan Xie, Yuhua Chen, Yongguo Yang, Jinhui Luo, Tieying Han

引用次数: 0

The Effects of Smoothing-Induced Weak Annotation in Mineral Prospectivity Mapping 平滑诱导弱注释在矿产找矿制图中的作用

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-08 DOI: 10.1007/s11053-026-10760-6

Steven E. Zhang, Mohammad Parsa

{"title":"The Effects of Smoothing-Induced Weak Annotation in Mineral Prospectivity Mapping","authors":"Steven E. Zhang, Mohammad Parsa","doi":"10.1007/s11053-026-10760-6","DOIUrl":"https://doi.org/10.1007/s11053-026-10760-6","url":null,"abstract":"The meaning of targets in data-driven mineral prospectivity mapping (DD-MPM) is foremost dependent on the quality of data. The resolving power of annotated data depends on the ratio of annotated area or volume and target footprint. At sufficiently large-scales of DD-MPM, it is impossible to match annotated area and covariate resolution to the footprint of mineral deposits. Consequently, information exposed to data modelling is unrestricted to between a target and its features. By definition, this type of annotation is a form of weak annotation, whose predictor–predictand relationship is weakened geostatistically through smoothing (a high ratio of annotated area or volume to target footprint). Annotation strength in DD-MPM is unstudied, although it is a research frontier in adjacent domains such as medical image segmentation. In this study, we present experimental results that reveal the spatial and aspatial significance of smoothing-induced weak annotation. Major findings include: (a) model performance improves with increasing smoothing, which is consistent with the linearization of the predictor–predictand relationship; (b) prospective area decreases with increasing smoothing, as smoothing weakens the signature of positive samples; (c) multifocal workflow uncertainty increases, caused by the same mechanism as in (b); and (d) the advantage of nonlinear and more expressive models is diminished with increasing smoothing. Therefore, although annotation strategy in DD-MPM seems innocuous, it has profound effects on the meaning of target area and exploration models. These effects impact: (a) the in-field validation of targets or model deployment expectations; (b) the mathematical meaning of trained models and targets; (c) the choice of model complexity; and (d) comparative analysis of workflow design (e.g., algorithms).","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"12 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149130456","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

Validation-Driven Weighted-Overlay Mineral Prospectivity Mapping of Western Afar Rift-Margin Epithermal Sulfide Systems: Integrated Geophysical–Geological Constraints 阿法尔西部裂谷边缘浅成热液硫化物系统验证驱动加权覆盖找矿填图：综合物探地质约束

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-07 DOI: 10.1007/s11053-026-10784-y

Asaye Getenet,Miao Luo,Xianhai Song,Adise Zemelak,Dessie Ayalew,Teame Haileslassie

引用次数: 0

A New Multivariate Analysis of Geothermal Assessment of Nigeria’s Crystalline Basement and Sedimentary Basins: Thermal and Geodynamic Implications 尼日利亚结晶基底和沉积盆地地热评价的一种新的多元分析：热学和地球动力学意义

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-05 DOI: 10.1007/s11053-026-10773-1

Samuel O. Sedara,Olusegun O. Alabi,Olorunyomi J. Adekanle,Oludayo O. Adekanye,Oche A. Christopher

引用次数: 0

Mechanisms of Water-Induced Mechanical Behavior in Coal: An Integrated Theoretical, Experimental, Numerical, and Field Investigation 煤中水诱发力学行为的机制：综合理论、实验、数值和实地调查

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-10-01 DOI: 10.1007/s11053-026-10776-y

Jifei Xu,Chengwu Li,Mingjie Li,Chen Chen,Zhenfei Li,Wenbo Xu

引用次数: 0

Adsorption Behavior and Energy Heterogeneity Evolution in Water-Bearing Shale 含水页岩吸附行为及能量非均质演化

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-09-29 DOI: 10.1007/s11053-026-10781-1

Zihao Chen,Bobo Li,Jianhua Li,Xianwei Zeng,Haosheng Song,Yunna Ding,Hongxia Zhuo

引用次数: 0

A Hybrid 3D CNN-GAT Framework with Entropy-Guided Adaptive Fusion for 3D Mineral Prospectivity Modeling 基于熵导自适应融合的三维CNN-GAT框架三维矿产远景建模

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-09-23 DOI: 10.1007/s11053-026-10780-2

Cong Chen,Mingming Zhang,Xiaoyuan Wang,Lexuan Wang,Xiaohui Li

{"title":"A Hybrid 3D CNN-GAT Framework with Entropy-Guided Adaptive Fusion for 3D Mineral Prospectivity Modeling","authors":"Cong Chen,Mingming Zhang,Xiaoyuan Wang,Lexuan Wang,Xiaohui Li","doi":"10.1007/s11053-026-10780-2","DOIUrl":"https://doi.org/10.1007/s11053-026-10780-2","url":null,"abstract":"Abstract Reliable three-dimensional (3D) mineral prospectivity modeling for concealed ore targeting remains challenging because voxel-based models emphasize local volumetric patterns, whereas nonlocal spatial relationships among sampled locations and prediction reliability are not jointly represented. To address this problem, this study developed a hybrid 3D CNN-graph attention network (GAT) framework for the Anqing skarn Cu district, eastern China. A ResNet3D-S&E branch extracts local structural patterns from 7 × 7 × 7 voxel patches, while a GATv2 branch represents coordinate-based neighborhood relationships among sampled voxels. Predictive entropy supports entropy-guided adaptive fusion and reliability-aware interpretation rather than full Bayesian uncertainty quantification. The full model achieved an area under the prediction rate curve of 0.942 and an area under the receiver operating characteristic curve of 0.964 on the validation dataset. The probability volume was converted into coherent exploration targets through smoothing, connected-component analysis, vertical-continuity filtering, geological-association screening, and spatial merging. Five distal prediction targets and one known-mineralization-supported reference target were delineated, linking 3D prospectivity prediction, predictive entropy, and geology-guided target interpretation.","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"11 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2026-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149049283","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

A Multi-Level Stacking Ensemble Architecture: Advantages of Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting Machine for Porphyry-Related Al-Based Mineral Prospectivity Mapping 多层次叠加集成体系结构：随机森林、极端梯度增强和光梯度增强机在斑岩相关铝基矿物远景定位中的优势

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-09-22 DOI: 10.1007/s11053-026-10763-3

Elnaz Geravandi,Reza Ghezelbash

引用次数: 0

Self-Supervised Multi-modal Fusion for Mineral Prospectivity Mapping Using Vision Transformers Integrating Multi-scale Geological Maps with Aeromagnetic Data 结合多比例尺地质图与航磁数据的视觉变换自监督多模态融合矿产找矿

IF 5.4 2区地球科学

Natural Resources Research Pub Date : 2026-09-22 DOI: 10.1007/s11053-026-10771-3

Sharon Christa,Ketut Tomy Suhari,Tushar Mane

引用次数: 0