Pub Date : 2025-12-23DOI: 10.1007/s11053-025-10605-8
Behrooz Esrafili-Dizaji
{"title":"Linking Flow Units to Sequence Stratigraphy in the Permian–Triassic Carbonate Reservoir of the South Pars Gas Field, Iran","authors":"Behrooz Esrafili-Dizaji","doi":"10.1007/s11053-025-10605-8","DOIUrl":"https://doi.org/10.1007/s11053-025-10605-8","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"126 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145830037","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":"Numerical Simulation of Gas Production and Geomechanical Response of Heterogeneous Gas Hydrate Reservoirs","authors":"Jiuhui Cheng, Shuitao Zhang, Linlin Wang, Guangjin Chen","doi":"10.1007/s11053-025-10577-9","DOIUrl":"https://doi.org/10.1007/s11053-025-10577-9","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"28 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807422","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}
Pub Date : 2025-12-22DOI: 10.1007/s11053-025-10564-0
Mohammad Parsa, Renato Cumani, Hossein Jodeiri Akbari Fam, Bilal Tawbe
Data-driven MPM (mineral prospectivity mapping) of critical minerals, which are elements or minerals with strategic importance and high supply chain risk, is vital for national land use planning. Recently, MPM has been practiced using supervised machine learning classification algorithms. However, applying such algorithms to Canadian critical minerals presents two major challenges. The first stems from the nature of geological knowledge, which is primarily stored in unstructured text. However, most supervised machine learning algorithms struggle to directly incorporate this textual information into predictive models. The second challenge arises from the limited number of known mineral deposits associated with many critical minerals in Canada, resulting in insufficient training labeled data for supervised classification tasks. To address the first challenge, this study employed natural language processing (NLP) techniques and large language models (LLMs) to extract and transform geoscientific knowledge embedded in geoscience text corpora into predictive features for MPM. LLMs operate based on transformer deep learning architectures that use self-attention mechanisms to capture contextual relationships within natural language. A domain-specific LLM, which was fine-tuned in this study and evaluated using geology-related inquiries, was employed for MPM. To address the second challenge, a separate transformer model was developed using a self-supervised learning approach that integrates diverse geophysical, geochronological, and textual data, eliminating the dependency on a substantial number of labeled training samples. The prospectivity model generated using the proposed transformer model significantly reduced the search space—by an average of 87%—for the targeted type of mineral deposits. The findings of this study demonstrate the effectiveness of transformer-based architectures and LLMs in overcoming key limitations of modern MPM approaches for critical mineral exploration.
{"title":"Large Language Models and Geoscience Transformers for Predictive Mapping of Canadian Critical Minerals","authors":"Mohammad Parsa, Renato Cumani, Hossein Jodeiri Akbari Fam, Bilal Tawbe","doi":"10.1007/s11053-025-10564-0","DOIUrl":"https://doi.org/10.1007/s11053-025-10564-0","url":null,"abstract":"Data-driven MPM (mineral prospectivity mapping) of critical minerals, which are elements or minerals with strategic importance and high supply chain risk, is vital for national land use planning. Recently, MPM has been practiced using supervised machine learning classification algorithms. However, applying such algorithms to Canadian critical minerals presents two major challenges. The first stems from the nature of geological knowledge, which is primarily stored in unstructured text. However, most supervised machine learning algorithms struggle to directly incorporate this textual information into predictive models. The second challenge arises from the limited number of known mineral deposits associated with many critical minerals in Canada, resulting in insufficient training labeled data for supervised classification tasks. To address the first challenge, this study employed natural language processing (NLP) techniques and large language models (LLMs) to extract and transform geoscientific knowledge embedded in geoscience text corpora into predictive features for MPM. LLMs operate based on transformer deep learning architectures that use self-attention mechanisms to capture contextual relationships within natural language. A domain-specific LLM, which was fine-tuned in this study and evaluated using geology-related inquiries, was employed for MPM. To address the second challenge, a separate transformer model was developed using a self-supervised learning approach that integrates diverse geophysical, geochronological, and textual data, eliminating the dependency on a substantial number of labeled training samples. The prospectivity model generated using the proposed transformer model significantly reduced the search space—by an average of 87%—for the targeted type of mineral deposits. The findings of this study demonstrate the effectiveness of transformer-based architectures and LLMs in overcoming key limitations of modern MPM approaches for critical mineral exploration.","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"22 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807421","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}
Pub Date : 2025-12-22DOI: 10.1007/s11053-025-10612-9
Miao Xie, Bingli Liu, Cheng Li, Yunhe Li
{"title":"Geo CNN-Trans: A Hybrid Deep Learning Framework for 3D Mineral Prospectivity Modeling","authors":"Miao Xie, Bingli Liu, Cheng Li, Yunhe Li","doi":"10.1007/s11053-025-10612-9","DOIUrl":"https://doi.org/10.1007/s11053-025-10612-9","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"73 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145807424","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}
Pub Date : 2025-12-21DOI: 10.1007/s11053-025-10614-7
Hongtao Zhao, Yu Zhang, Yongjun Shao, Yuanming Pan, Ana Carolina R. Miranda, Yanbo Xu, Yuzhou Feng, Xiaoyan Chen
{"title":"Pyrite Geochemistry of the Qianchen Gold Deposit, Jiaodong Peninsula, China: An Indicator Mineral for the Spatial Evolution of Mineralization Fluid","authors":"Hongtao Zhao, Yu Zhang, Yongjun Shao, Yuanming Pan, Ana Carolina R. Miranda, Yanbo Xu, Yuzhou Feng, Xiaoyan Chen","doi":"10.1007/s11053-025-10614-7","DOIUrl":"https://doi.org/10.1007/s11053-025-10614-7","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"56 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796235","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}
Pub Date : 2025-12-19DOI: 10.1007/s11053-025-10597-5
Xiaoqiang Zhu, Yongjian Gu, Shuai Zhang, Yanwen Zhang, Cai Jia, Haiyang He
{"title":"Mineral Prospectivity Mapping via Interpretable Machine Learning Techniques: A Case Study in the Tongling Ore District, China","authors":"Xiaoqiang Zhu, Yongjian Gu, Shuai Zhang, Yanwen Zhang, Cai Jia, Haiyang He","doi":"10.1007/s11053-025-10597-5","DOIUrl":"https://doi.org/10.1007/s11053-025-10597-5","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"27 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145796257","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}
Pub Date : 2025-12-15DOI: 10.1007/s11053-025-10598-4
Jiang Dexin, Du Xiaofeng, Liu Shixiang, Li Chunrong, Wu Yunfei, Li Yican, Yang Tianbo, Li Fanyi
{"title":"Reserve Growth Modeling and Prediction in Oil Fields of the Bohai Bay Basin, China","authors":"Jiang Dexin, Du Xiaofeng, Liu Shixiang, Li Chunrong, Wu Yunfei, Li Yican, Yang Tianbo, Li Fanyi","doi":"10.1007/s11053-025-10598-4","DOIUrl":"https://doi.org/10.1007/s11053-025-10598-4","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"56 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145752831","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}
Pub Date : 2025-12-14DOI: 10.1007/s11053-025-10613-8
Dongping Zhao, Songhang Zhang, Shuheng Tang, Wenguang Tian, Ke Zhang, Kun Zhang
{"title":"Aquifer Delineation in the Benxi Formation and Its Critical Impact on Deep CBM Productivity: A Case Study of the Central-Eastern Ordos Basin","authors":"Dongping Zhao, Songhang Zhang, Shuheng Tang, Wenguang Tian, Ke Zhang, Kun Zhang","doi":"10.1007/s11053-025-10613-8","DOIUrl":"https://doi.org/10.1007/s11053-025-10613-8","url":null,"abstract":"","PeriodicalId":54284,"journal":{"name":"Natural Resources Research","volume":"7 1","pages":""},"PeriodicalIF":5.4,"publicationDate":"2025-12-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145753183","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}
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