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

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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Knowledge Graph for Identifying Geological Disasters by Integrating Computer Vision with Ontology

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地球科学学刊:英文版 2023年第5期34卷 1418-1432页

作者： Qinjun Qiu Zhong Xie Die Zhang Kai Ma Liufeng Tao Yongjian Tan Zhipeng Zhang Baode Jiang School of Computer science China University of GeosciencesWuhan 430078China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China National Engineering Research Center of Geographic Information System Wuhan 430074China School of Resource and Environmental sciences Wuhan UniversityWuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China

The occurrence of geological disasters can have a large impact on urban safety. Protecting people’s safety is the most important concern when disasters occur. Safety improvement requires a large amount of comprehensive and representative risk analysis and a large collection of information related to geological hazards, including unstructured knowledge and experience. To address the relevant information and support safety risk analysis, a geological hazard knowledge graph is developed automatically based on computer vision and domain-geoscience ontology to identify geological hazards from input images while obeying safety rules and regulations, even when affected by changes. In the implementation of the knowledge graph, we design an ontology schema of geological disasters based on a top-down approach, and by organizing knowledge as a logical semantic expression, it can be shared using ontology technologies and therefore enable semantic interoperability. Computer vision approaches are then used to automatically detect a set of entities and attributes, using the data from input images, and object types and their attributes are identified so that they can be stored in Neo4j for reasoning and searching. Finally, a reasoning model for geological hazard identification was developed using the Neo4j database to create nodes, relationships, and their properties for modeling, and geological hazards in the images can be automatically identified by searching the Neo4j database. An application on geological hazard is presented. The results show the effectiveness of the proposed approach in terms of identifying possible potential hazards in geological hazards and assisting in formulating targeted preventive measures.

关键词： geological hazard computer vision knowledge graph city safety ontology

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A Comparison of Nd Isotopes of Granitoids from the Central Asian Orogenic Belt and Qinling-Dabie Orogen,and Implications for Understanding of Crustal Growth from Accretion to Collision

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Acta Geologica Sinica(English Edition) 2019年第S1期93卷 150-151页

作者： WANG Tao WANG Xiaoxia TONG Ying HUANG He LI Shan ZHANG Jianjun GUO Lei ZHANG Lei SONG Peng QIN Qie Key Laboratory of deep-Earth Dynamics Ministry of Natural ResourcesInstitute of Geology Chinese Academy of Geological SciencesBeijing 100037China Beijing SHRIMP Center Beijing 100037China MLR Key Laboratory of Metallogeny and Mineral Assessment Institute of Mineral ResourcesChinese Academy of Geological SciencesBeijing 100037China

Orogens can generally be divided into two types:accretionary and *** fundamental differences in deep-crustal compositions and architecture from accretion to collision and how to identify them is not well *** is one of... 详细信息

关键词： Granitoids accretionary orogeny juvenile crust Central Asian Orogenic Belt

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Nd Isotopic Characteristics of Granitoids from the New foundland Appalachians

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Acta Geologica Sinica(English Edition) 2019年第S1期93卷 147-149页

作者： WANG Chaoyang WANG Tao Cees Van STAAL LIN Shoufa ZHANG Jianjun Key Laboratory of deep-Earth Dynamics Ministry of Natural ResourcesInstitute of Geology China Academy of Geological SciencesBejing 100037China Beijing SHRIMP Center Bejing 100037China Geological Survey of Canada 1500-605 Robson StreetVancouverBC V6B 5J3Canada Department of Earth and Environmental sciences University of WaterlooWaterlooOntarioCanada

The Appalachian orogen in North America is currently considered to be a Paleozoic accretion-type orogenic belt,or a collage,formed by collision of many ancient blocks between Laurentian and Gondwanan margins(Williams,1979;Williams et al.,1988;van Staal et al.,2007).Recently,major progress has been made in understanding the characteristics and tectonic evolution of the outboard peri-Laurentian and peri-Gondwanan terranes of the Iapetus Ocean(van Staal et al.,2009,2012).

关键词： Granitoids Nd isotopes Crustal compositions Newfoundland Appalachian orogen

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Integrating NLP and Ontology Matching into a Unified System for Automated Information Extraction from Geological Hazard Reports

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地球科学学刊:英文版 2023年第5期34卷 1433-1446页

作者： Qinjun Qiu Zhen Huang Dexin Xu Kai Ma Liufeng Tao Run Wang Jianguo Chen Zhong Xie Yongsheng Pan Key Laboratory of Urban Land Resources Monitoring and Simulation Ministry of Natural ResourcesShenzhen 518034China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Beijing Key Laboratory of Urban Spatial Information Engineering Beijing 100045China School of Geography and Information Engineering China University of GeosciencesWuhan 430074China Wuhan Geomatics Institute Wuhan 430074China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China Geological Environmental Center of Hubei Province Wuhan 430034China Faculty of Earth Resources China University of GeosciencesWuhan 430074China State Key Laboratory of Geological Processes and Mineral Resources China University of GeosciencesWuhan 430078China Wuhan Zondy Cyber science&Technology Co. Ltd.Wuhan 430074China

Many detailed data on past geological hazard events are buried in geological hazard reports and have not been fully utilized. The growing developments in geographic information retrieval and temporal information retrieval offer opportunities to analyse this wealth of data to mine the spatiotemporal evolution of geological disaster occurrence and enhance risk decision making. This study presents a combined NLP and ontology matching information extraction framework for automatically recognizing semantic and spatiotemporal information from geological hazard reports. This framework mainly extracts unstructured information from geological disaster reports through named entity recognition, ontology matching and gazetteer matching to identify and annotate elements, thus enabling users to quickly obtain key information and understand the general content of disaster reports. In addition, we present the final results obtained from the experiments through a reasonable visualization and analyse the visual results. The extraction and retrieval of semantic information related to the dynamics of geohazard events are performed from both natural and human perspectives to provide information on the progress of events.

关键词： geological hazard report spatiotemporal information geological hazard ontology natural language processing gazetteers onlology machine learning

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A Practical Approach to Constructing a Geological Knowledge Graph:A Case Study of Mineral Exploration Data

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地球科学学刊:英文版 2023年第5期34卷 1374-1389页

作者： Qinjun Qiu Bin Wang Kai Ma Hairong Lü Liufeng Tao Zhong Xie Collaborative Innovation Center of Geo-Information Technology for Smart Central Plains Zhengzhou 450046China Key Laboratory of Spatiotemporal Perception and Intelligent Processing Ministry of Natural ResourcesZhengzhou 450046China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Department of Automation Tsinghua UniversityBeijing 100084China

Open data initiatives have promoted governmental agencies and scientific organizations to publish data online for *** of geoscience focuses on processing georeferenced quantitative data(e.g.,rock parameters,geochemical tests,geophysical surveys and satellite imagery)for discovering new *** knowledge is the cognitive result of human knowledge of the spatial distribution,evolution and interaction patterns of geological objects or *** graphs(KGs)can formalize unstructured knowledge into structured form and have been used in supporting decision-making *** this paper,we propose a novel framework that can extract the geological knowledge graph(GKG)from public reports relating to a modelling *** on the analysis of basic questions answered by geology,we summarize and abstract geological knowledge elements and then explore a geological knowledge representation model with three levels of“geological conceptsgeological entities-geological relations”to describe semantic units of geological knowledge and their logic ***,based on the characteristics of mineral resource reports,the geological knowledge representation model oriented to“object relationships”and the hierarchical geological knowledge representation model oriented to“process relationships”are proposed with reference to the commonly used geological knowledge graph *** research in this paper can provide some implications for the formalization and structured representation of geological knowledge graphs.

关键词： mineral resource report geological knowledge knowledge graph ontology hierarchical knowledge representation model

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Extracting Named Entity Using Entity Labeling in Geological Text Using deep Learning Approach

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地球科学学刊:英文版 2023年第5期34卷 1406-1417页

作者： Qinjun Qiu Miao Tian Zhong Xie Yongjian Tan Kai Ma Qingfang Wang Shengyong Pan Liufeng Tao State Key Laboratory of Geo-Information Engineering and Key Laboratory of Surveying and Mapping science and Geospatial Information Technology of MNR Chinese Academy of Surveying and MappingBeijing 100036China Key Laboratory of Spatial-Temporal Big Data Analysis and Application of Natural Resources in Megacities MNRShanghai 20063China Key Laboratory of Geological Survey and Evaluation of Ministry of Education China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Intelligent Vision Based Monitoring for Hydroelectric Engineering China Three Gorges UniversityYichang 443002China College of Computer and Information Technology China Three Gorges UniversityYichang 443002China Chengdu Geological Environment Monitoring Station Chengdu 610036China Wuhan Zondy Cyber science&Technology Co. Ltd.Wuhan 430074China

Artificial intelligence(AI) is the key to mining and enhancing the value of big data, and knowledge graph is one of the important cornerstones of artificial intelligence, which is the core foundation for the integration of statistical and physical representations. Named entity recognition is a fundamental research task for building knowledge graphs, which needs to be supported by a high-quality corpus, and currently there is a lack of high-quality named entity recognition corpus in the field of geology, especially in Chinese. In this paper, based on the conceptual structure of geological ontology and the analysis of the characteristics of geological texts, a classification system of geological named entity types is designed with the guidance and participation of geological experts, a corresponding annotation specification is formulated, an annotation tool is developed, and the first named entity recognition corpus for the geological domain is annotated based on real geological reports. The total number of words annotated was 698 512 and the number of entities was 23 345. The paper also explores the feasibility of a model pre-annotation strategy and presents a statistical analysis of the distribution of technical and term categories across genres and the consistency of corpus annotation. Based on this corpus, a Lite Bidirectional Encoder Representations from Transformers(ALBERT)-Bi-directional Long Short-Term Memory(BiLSTM)-Conditional Random Fields(CRF) and ALBERT-BiLSTM models are selected for experiments, and the results show that the F1-scores of the recognition performance of the two models reach 0.75 and 0.65 respectively, providing a corpus basis and technical support for information extraction in the field of geology.

关键词： ontology geological reports named entity recognition geological corpus construction semi-automated annotation platforms deep learning

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A knowledge graph for standard carbonate microfacies and its application in the automatical reconstruction of the relative sea-level curve

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Geoscience Frontiers 2023年第5期14卷 402-414页

作者： Han Wang Hanting Zhong Anqing Chen Keran Li Hang He Zhe Qi Dongyu Zheng Hongyi Zhao Mingcai Hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Chengdu University of TechnologyChengdu 610059China Key Laboratory of deep-time Geography and Environment Reconstruction and Applications MNR&Institute of Sedimentary GeologyChengdu University of TechnologyChengdu 610059China State Key Laboratory of Biogeology and Environmental Geology China University of Geosciences(Beijing)Beijing 100083China

The reconstruction of high-resolution sea-level variation curves in deep time based on the standard car-bonate microfacies knowledge graph(SMFKG)is of great scientific significance for exploring the Earth system evolution and predicting future sea-level and climate *** this study,the concepts,attri-butes,and relationships among standard carbonate microfacies(SMF)are comprehensively analyzed;an ontology layer is established and its data layer is constructed using thin-section descriptions;and finally,the SMFKG is ***,based on the knowledge graph,an application for automatically identifying SMF using identification markers and reconstructing the high-resolution relative sea-level variation curve using the SMF and facies zones is ***,all thin sections of the late Ediacaran Dengying Formation in the western margin of the Yangtze Platform are observed and described in detail,the SMF and facies zones are identified automatically,and the relative sea-level curve is recon-structed automatically using the *** reconstruction results show that the Yangtze Platform experienced four sea-level rise and fall cycles in the late Ediacaran,of which two intense regressions led to subaerial-exposed unconformities in the interior and top of the Dengying Formation,which is highly consistent with previous research *** shows that the high-resolution relative sea-level variation curve in deep time can be reconstructed efficiently and intelligently using the ***,in the near future,the combination of an automatic digital slide-scanning system,machine-learning techniques,and the SMFKG can achieve one-stop fully automatic SMF recognition and reconstruction of high-resolution relative sea-level variation curves in deep time,which has a high application value.

关键词： Sea-level curve Standard microfacies Knowledge graph Ediacaran Dengying Formation

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