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Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

Journal of Earth Science 2024年第3期35卷 1035-1043页

作者： Hang He Chao Ma Shan Ye Wenqiang Tang Yuxuan Zhou Zhen Yu Jiaxin Yi Li hou mingcai hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyCollege of Computer Science and Cyber SecurityChengdu University of TechnologyChengdu 610059China Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of TechnologyChengdu 610059China School of Information Engineering China University of Geosciences(Beijing)Beijing 100083China

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual *** the rapid development of science and technology,a large number of textual reports have accumulated in the field of ***,many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining,making it more challenging for some researchers to extract necessary information from these *** Language Processing(NLP)has obvious advantages in processing large amounts of textual *** objective of this paper is to identify geological named entities from Chinese geological texts using NLP *** propose the RoBERTa-Prompt-Tuning-NER method,which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset *** RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word ***,we conducted experiments on the constructed Geological Named Entity Recognition(GNER)*** experimental results show that the proposed model achieves the highest F1 score of 80.64%among the four baseline algorithms,demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

关键词： Prompt Learning Named Entity Recognition(NER) low resource geological text text information mining big data geology.

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The Early-Middle Triassic Supervolcano in the Yangtze Block,South China and associated obstacles to biotic recovery

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Geoscience Frontiers 2024年第1期15卷 69-83页

作者： Haifeng Chen Hao Zou MSantosh Huawen Cao Franco Pirajno Changcheng Huang mingcai hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of TechnologyChengduSichuan 610059China College of Earth Science Chengdu University of TechnologyChengduSichuan 610059China School of Earth Sciences and Resources China University of GeosciencesBeijing 100083China Department of Earth Science University of AdelaideAdelaideSA 5005Australia Yonsei Frontier Lab Yonsei UniversitySeoulSouth Korea Centre of Exploration Targeting The University of Western Australia35 Stirling HighwayCrawleyWA 6009Australia

The end-Permian mass extinction was one of the major global crises spanning the entire Early Triassic or *** of volcanos were one of the factors that delayed the biotic recovery after this *** eruptions can cause catastrophic effects on global environment,climate,and *** we investigate the tuff layers from Early-Middle Triassic boundary in the Yangtze Block and identify a supervolcano eruption *** zircon U-Pb ages of the section-Langdai,section-Daijiagou and section-Longmendong tuff samples are 247.1±1.9 Ma,247.6±2.0 Ma and 247.7±1.7 Ma,*** ages mark the Olenekian-Anisian *** zircon grains from the tuff layers have negativeεHf(t)(-15.3 to-0.8),two-stage Hf model(TDM2)ages(1.7 to 2.2 Ga)and display high-δ18O values(mostly>10‰).Clay minerals and quartz dominate the rock *** whole rock compositions show that the tuff layers were derived from magma of intermediate to felsic composition,which formed by the remelting of Paleoproterozoic materials of continental *** volcanic eruption site is located in the Jinshajiang-Ailaoshan-Song Ma suture zone in the southwestern margin of the Yangtze Block.A combination of the closure of the Paleo-Tethys Ocean Basin and the collision of the Indochina Block and South China contributed to the eruption,which was a supervolcano eruption under the active continental margin arc *** speculate that this supervolcano eruption might have contributed to the delayed biotic recovery after the end-Permian mass extinction.

关键词： Early-Middle Triassic Tuff layer Supervolcano Yangtze Block Biotic recovery

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全球硅酸盐岩化学风化—二氧化碳消耗定量模型研究进展

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古地理学报 2024年第2期26卷 460-474页

作者：白洁郑栋宇侯明才陈安清马超成都理工大学沉积地质研究院四川成都610059

硅酸盐岩通过与二氧化碳的化学反应,去除大气二氧化碳并将其封存在风化产物或海洋碳酸盐岩中,是影响全球碳循环以及气候变化的要素之一。定量计算全球硅酸盐岩通过风化作用消耗的二氧化碳总量是了解地球现今与过去气候变化的关键。作者... 详细信息

硅酸盐岩通过与二氧化碳的化学反应,去除大气二氧化碳并将其封存在风化产物或海洋碳酸盐岩中,是影响全球碳循环以及气候变化的要素之一。定量计算全球硅酸盐岩通过风化作用消耗的二氧化碳总量是了解地球现今与过去气候变化的关键。作者系统调研了5个硅酸盐岩化学风化—二氧化碳消耗定量模型的数据来源、研究方法、计算公式以及各模型的主要影响因素,并且以最新的Celine模型所计算得出的二氧化碳消耗量为参考标准,对比了各模型的优缺点与适用范围。现有模型估计全球硅酸盐岩化学风化的二氧化碳消耗量为69~169 Tg/yr,其中各模型的主要参数包括气候(温度、径流)与岩性,次要参数包括构造隆升、火山与植物作用等。在未来探索硅酸盐岩化学风化所消耗二氧化碳的定量计算中,应考虑更多控制作用的影响以及各因素之间的相互联系。此外,利用大数据分析方法将这些定量模型推广应用于深时地球古气候重建可能是未来的研究趋势。

关键词：硅酸盐岩化学风化二氧化碳消耗定量模型全球碳循环气候变化古气候重建

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琼东南盆地二叠纪-三叠纪长英质岩浆作用:对南海北部陆缘构造属性与演化过程的启示

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岩石学报 2024年第8期40卷 2450-2468页

作者：徐长贵侯明才吴克强熊富浩尤丽李成祥王文博中国海洋石油有限公司北京101000 成都理工大学成都610059 中海石油(中国)有限公司海南分公司海口570100

琼东南盆地发育二叠纪-三叠纪岩浆岩类,为研究南海北部陆缘古构造属性与大地构造演化过程提供了岩石探针。本文以基底长英质岩浆岩为研究对象,开展岩石学、锆石U-Pb年代学、岩石地球化学和锆石Lu-Hf同位素研究,探讨琼东南盆地二叠纪-三... 详细信息

琼东南盆地发育二叠纪-三叠纪岩浆岩类,为研究南海北部陆缘古构造属性与大地构造演化过程提供了岩石探针。本文以基底长英质岩浆岩为研究对象,开展岩石学、锆石U-Pb年代学、岩石地球化学和锆石Lu-Hf同位素研究,探讨琼东南盆地二叠纪-三叠纪岩浆岩的时空分布、岩石成因及动力学背景。LA-ICP-MS锆石U-Pb年代学表明,琼东南盆地发育二叠纪-早三叠世、中三叠世和晚三叠世三期长英质岩浆作用(271~217Ma),且岩浆活动具有自南向北迁移的趋势。岩石地球化学分析揭示,二叠纪-早三叠世长英质岩具有较低的FeO/MgO和10000×Ga/Al比值,且强烈亏损Zr、Hf、Nb、Ta等元素,属于典型的陆缘弧I型花岗岩类;中三叠世和晚三叠世岩浆岩均具有较高的SiO_(2)、Mg^(#)、10000×Ga/Al和FeO/MgO比值,大多属于A型花岗岩类。同时,二叠纪-早三叠世长英质岩具有较低的Hf同位素组成(ε_(Hf)(t)=-7.61~-0.65),而中-晚三叠世长英质岩则大多具有较高的Hf同位素组成。岩石成因分析揭示,二叠纪-早三叠世I型花岗岩类起源于古老陆壳的部分熔融,而中三叠世和晚三叠世A型花岗岩类则部分起源于古老长英质地壳熔融,部分则起源于新生地壳熔融,并伴随有壳幔相互作用。综合分析表明,二叠纪-三叠纪期间南海北部陆缘主要受控于古特提斯造山作用控制,且先后经历了二叠纪至早三叠世的古特提斯洋壳俯冲、中三叠世印支板块与华南板块碰撞以及晚三叠世碰撞后伸展等动力学过程。

关键词：琼东南盆地二叠纪-三叠纪长英质岩岩石成因构造演化古特提斯

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琼东南盆地花岗质岩浆-构造演化及其对潜山储层物性的约束

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地质通报 2024年第7期43卷 1191-1206页

作者：胡林熊富浩陈安清周杰王文博侯明才中海石油(中国)有限公司海南分公司海南海口570311 油气藏地质及开发工程全国重点实验室成都理工大学四川成都610059

琼东南盆地中生代潜山是认识南海西部陆缘构造演化过程的重要窗口。以琼东南盆地陵南与松南低凸起区的花岗岩潜山为研究对象,开展岩心样品的岩相学、年代学与地球化学研究,揭示其岩石成因及构造环境。研究结果表明,陵南低凸起基岩主要... 详细信息

琼东南盆地中生代潜山是认识南海西部陆缘构造演化过程的重要窗口。以琼东南盆地陵南与松南低凸起区的花岗岩潜山为研究对象,开展岩心样品的岩相学、年代学与地球化学研究,揭示其岩石成因及构造环境。研究结果表明,陵南低凸起基岩主要为二长花岗岩,松南低凸起基岩为花岗闪长岩与二长花岗岩。LA-ICP-MS锆石U-Pb年龄揭示,陵南低凸起区花岗岩类分别形成于249 Ma和233 Ma,而松南低凸起区花岗岩类形成于222~219 Ma。陵南低凸起区花岗岩类的铝饱和指数(A/CNK)为0.99~1.16,岩石整体富集Rb、Th、U等大离子亲石元素,亏损Ta、Nb、Ti等高场强元素,属于弱过铝质高钾钙碱性I型花岗岩;松南低凸起区花岗岩类富碱(K_(2)O+Na_(2)O=6.74%~8.41%)、贫铝(Al_(2)O_(3)=12.52%~13.70%),具有较高的Rb/Sr值(2.03~9.20)和10000 Ga/Al值(>6),且岩石富集轻稀土元素,并具明显的负Eu异常,属于典型的A型花岗岩。岩石成因分析表明,琼东南盆地三叠纪花岗岩类起源于变沉积岩的部分熔融,且经历了不同程度的壳幔混合作用和分离结晶作用。琼东南盆地早三叠世花岗岩类形成于古特提斯域的俯冲汇聚和同碰撞环境,而晚三叠世花岗岩类形成于碰撞后伸展背景。琼东南盆地早三叠世花岗岩不仅含有较高比例的长英质脆性矿物,同时经历了俯冲碰撞-碰撞后伸展等多期次构造作用改造,具备形成裂缝型潜山储层的物质基础和构造动力学条件,是南海潜山油气勘探的重要对象。

关键词：琼东南盆地花岗岩类潜山锆石U-Pb年龄构造环境

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辽东湾海域新元古界长龙山组石英砂岩物源特征及其地质意义

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沉积学报 2024年第4期42卷 1342-1353页

作者：赵子霖周雪威李夔洲郭涛彭靖松侯明才成都理工大学沉积地质研究院&深时地理环境重建与应用自然资源部重点实验室成都610059 油气藏地质及开发工程全国重点实验室(成都理工大学) 成都610059 中海石油(中国)有限公司天津分公司天津300452

【目的】渤海辽东湾海域西南部钻井钻遇新元古界青白口系长龙山组石英砂岩,深入研究其物源特征,对华北克拉通太古代至元古代重大地质事件解剖具有重大意义。【方法】对采自BD4-1a井2482 m附近的五件石英砂岩样品进行了全岩地球化学和碎... 详细信息

【目的】渤海辽东湾海域西南部钻井钻遇新元古界青白口系长龙山组石英砂岩,深入研究其物源特征,对华北克拉通太古代至元古代重大地质事件解剖具有重大意义。【方法】对采自BD4-1a井2482 m附近的五件石英砂岩样品进行了全岩地球化学和碎屑锆石U-Pb年代学分析。【结果】全岩地球化学分析结果显示该石英砂岩具有较高的SiO2含量(88.53%~93.06%),化学蚀变指数(CIA)值介于69.5~71.6,成分变异指数(ICV)值介于0.69~3.99,样品表现为高成分成熟度和结构成熟度、中等风化。Th/U值介于3.46~4.92,Cr/Zr值介于0.17~0.35,ΣREE值介于(8.26~24.12)×10^(-6),LREE/HREE值介于6.28~10.21,δEu值介于0.77~1.04,呈弱负异常。碎屑锆石U-Pb年龄分布于2744~1581 Ma,主要年龄峰值为~1845 Ma,次要年龄峰值为~2225 Ma、~2340 Ma、~2600 Ma和~2725 Ma。【结论】全岩地球化学分析结果显示其物源主要来自于上地壳长英质岩石。2.7~2.5 Ga的碎屑锆石是华北克拉通陆壳生长事件及构造热事件形成的记录;2.2~1.8 Ga的碎屑锆石与华北克拉通东部陆块和西部陆块拼合形成同一结晶基底事件的时间相吻合,是哥伦比亚超大陆聚合事件的地质记录;而1.8~1.6 Ga的碎屑锆石年龄记录的应该是哥伦比亚超大陆裂解事件。

关键词：渤海碎屑锆石地球化学物源哥伦比亚超大陆

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Zircon classification from cathodoluminescence images using deep learning

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Geoscience Frontiers 2022年第6期13卷 111-121页

作者： Dongyu Zheng Sixuan Wu Chao Ma Lu Xiang Li hou Anqing Chen mingcai hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of TechnologyChengdu 610051PR China Key Laboratory of Deep-time Geography&Environment Reconstruction and Applications of Ministry of Natural Resources&Institute of Sedimentary Geology Chengdu University of TechnologyChengdu 610051PR China Program of Geology and Geophysics Missouri University of Science and TechnologyRolla 65401USA College of Earth Sciences Chengdu University of TechnologyChengdu 610051PR China

Zircon is a widely-used heavy mineral in geochronological and geochemical research because it can extract important information to understand the history and genesis of rocks. Zircon has various types,and an accurate examination of zircon type is a prerequisite procedure before further ***(CL) imaging is one of the most reliable ways to classify zircons. However, current CL image examination is conducted by manual work, which is time-consuming, bias-prone, and requires expertise. An automated and bias-free method for zircon classification is absent but necessary. To this end, deep convolutional neural networks(DCNNs) and transfer learning are applied in this study to classify the common types of zircons, i.e., igneous, metamorphic, and hydrothermal zircons. An atlas with over 4000 CL images of these three types of zircons is created, and three DCNNs are trained using these images. The results of this study indicate that the DCNNs can distinguish hydrothermal zircons from other zircons, as indicated by the highest accuracy of 100%. Although similar textures in igneous and metamorphic zircons pose great challenges for zircon classification, the DCNNs successfully classify 95% igneous and 92% metamorphic zircons. This study demonstrates the high accuracy of DCNNs in zircon classification and presents the great potentiality of deep learning techniques in numerous geoscientific disciplines.

关键词： Zircon Cathodoluminescence image Deep learning Transfer learning

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Climate paleogeography knowledge graph and deep time paleoclimate classifications

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Geoscience Frontiers 2023年第5期14卷 362-372页

作者： Chenmin Yu Laiming Zhang mingcai hou Jianghai Yang Hanting Zhong Chengshan Wang State Key Laboratory of Biogeology and Environmental Geology School of the Earth Science and ResourcesChina University of GeosciencesBeijing 100083China Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications MNR&Institute of Sedimentary GeologyChengdu University of TechnologyChengdu 610059China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyChengdu University of TechnologyChengdu 610059China State Key Laboratory of Biogeology and Environmental Geology School of Earth SciencesChina University of GeosciencesWuhan 430074China

The climate paleogeography,especially the climate classifications,helps to interpret the global and regional climate changes and intuitively compare the climate conditions in different ***,the application of climate classification in deep time(i.e.,climate paleogeography)is prohibited due to the usually qualitatively constrained paleoclimate and the inconsistent descriptions and semantic heterogeneity of the climate *** this study,a climate paleogeography knowledge graph is established under the framework of the Deep-Time Digital Earth program(DDE).The hierarchical knowledge graph consists of five paleoclimate classifications based on various *** classifications are described and their strengths and weaknesses are fully evaluated in four aspects:“simplicity,applicability,quantifiability,and comparability”.We also reconstruct the global climate distributions in the Late Cretaceous according to these *** results are compared and the relationships among these climate types in different classifications are *** study unifies scientific concepts from different paleoclimate classifications,which provides an important theoretical basis for the application of paleoclimate classifications in deep time.

关键词： Climate paleogeography Knowledge graph Paleoclimate classification Deep-Time Digital Earth program

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Construction of a fluvial facies knowledge graph and its application in sedimentary facies identification

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Geoscience Frontiers 2023年第2期14卷 268-281页

作者： Lei Zhang mingcai hou Anqing Chen Hanting Zhong James G.Ogg Dongyu Zheng Key Laboratory of Deep-time Geography and Environment Reconstruction and Applications MNR&Institute of Sedimentary GeologyChengdu University of TechnologyChengdu 610059China State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation(Chengdu University of Technology) Chengdu 610059China

Lithofacies paleogeography is a data-intensive discipline that involves the interpretation and compilation of sedimentary *** sedimentary facies analysis is a labor-intensive task with the added complexity of using unstructured knowledge and unstandardized ***,it is very difficult for beginners or non-geology scholars who lack a systematic knowledge and experience in sedimentary facies *** hurdles could be partly alleviated by having a standardized,structured,and systematic knowledge base coupled with an efficient automatic machine-assisted sedimentary facies identification *** this end,this study constructed a knowledge system for fluvial facies and carried out knowledge *** include a domain knowledge graph for types of fluvial facies(meandering,braided and other fluvial depositional environments)and their characteristic features(bedforms,grain size distribution,etc.)with visualization,a method for query and retrieval on a graph database platform,a hierarchical knowledge tree-structure,a data-mining clustering algorithm for machineanalysis of publication texts,and an algorithm model for this area of sedimentary facies *** underlying sedimentary facies identification and knowledge reasoning system is based on expert experience and synthesis of *** testing,17 sets literature publications data that included details of sedimentary facies data(bedforms,grain sizes,etc.)were submitted to the artificial intelligence model,then compared and *** testing set of automated reasoning results yielded an interpretation accuracy of about 90%relative to the published interpretations in those ***,the model and algorithm provide an efficient and automated reasoning technology,which provides a new approach and route for the rapid and intelligent identification of other types of sedimentary facies from literature data or direct use in the field.

关键词： Fluvial facies Knowledge graph Domain ontology Sedimentary facies identification Machine-assisted interpretation

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新时代行业特色高校创新型人才培养供给侧改革思考——以地质行业高校为例

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中国地质教育 2024年第1期33卷 29-33页

作者：施泽明徐争启邹灏侯明才宋昊杨毅成都理工大学地球科学学院四川成都610059 地学核技术四川省重点实验室四川成都610059

行业特色高校在长期办学过程中形成了与行业密切相关的办学特色和优势学科。在新时代中国高等教育发展改革过程中,深入研究地质行业特色高校高质量发展和创新型人才培养的现状、存在问题和实现路径,促进行业特色高校丰富内涵和可持续发... 详细信息

行业特色高校在长期办学过程中形成了与行业密切相关的办学特色和优势学科。在新时代中国高等教育发展改革过程中,深入研究地质行业特色高校高质量发展和创新型人才培养的现状、存在问题和实现路径,促进行业特色高校丰富内涵和可持续发展,是“双一流”建设的重要内容,也是国家创新发展的保障。随着地质行业的转型发展,地质行业高校创新型人才培养必须适应国家发展。这就需要地质行业高校不仅从需求侧进行人才培养,而且要从供给侧进行人才培养的探索。本文基于新时代高校人才培养要求,聚焦地质行业发展需要,从供给侧角度探索地质行业高校创新型人才培养的需求、存在的问题、改革的关键及实现途径,为行业高校人才培养供给侧改革提供参考和借鉴。

关键词：供给侧改革人才培养地质行业行业高校

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