Table of Content

20 February 2020, Volume 26 Issue 1

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Construction of Earth Science Knowledge Graph and Its Future Perspectives

QI Hao, DONG Shaochun, ZHANG Lili, HU Huan, FAN Junxuan

2020, 26(1):  2.  DOI: 10.16108/j.issn1006-7493.2019099

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Big data have brought innovations as well as challenges to Earth science research. However, due to inconsistent data description standards, unclear data sharing mechanism and significant semantic heterogeneity, there are significant difficulties existed in big data integration, sharing and reuse in Earth science research. The knowledge graph can be used to explicitly represent concepts and their complex semantic relationships in a machine-understandable way. Therefore, it has been widely applied for semantic translation, data integration and data reuse. In order to establish Earth science knowledge graph, the current paper analyzes the characteristics of the existing knowledge graphs and investigates the main construction methods. Relationships of data dictionary, knowledge system and knowledge graph are also illustrated and analyzed. Current Earth science thematic databases and domain ontologies have also been reviewed, and future perspectives on Earth science knowledge graph applications are provided.

Review of Igneous Rock Databases and Their Application Prospect

ZHANG Yinghui, WANG Tao, JIAO Shoutao, GUO Lei, FAN Runlong, WANG Yanggang, ZHANG Jianjun

2020, 26(1):  11.  DOI: 10.16108/j.issn1006-7493.2019097

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More than two-thirds of the Earth crust rocks are formed by magmatism from the deep. The information recorded by igneous rocks are important resource for understanding deep process, as well as evolution of the Earth during deep-time, and therefore valuable for the Deep-time Digital Earth (DDE) Big Science Program. Igneous rocks are ideal candidates for data accumulation because of their wide distribution, significant amount and rather accurate dating. Over the past decade, scientists around the world have established excellent igneous rock databases such as EarthChem, GEOROC and DataView. All have their virtues and defects. Under the DDE project, to accumulate igneous rock data scattered in research institutions and individuals, and establish a highly integrated database along with a platform suited with artificial intelligence algorithms, will be a breakthrough point. They will promote the Earth science research from traditional theory-driven searching to big data-driven discovering. The current paper reviews the existing igneous rock databases, including their data, systems and operations, in order to provide reference for the future DDE igneous rock database. Several big data-driven researches on igneous rock during recent years have also been reviewed, to explore all aspects of future research when igneous rock big data is combined with AI technique.

Advances on Sedimentary Database Building and Related Research: Macrostrat As an Example

JIANG Jingxin, LI Chao, HU Xiumian

2020, 26(1):  27.  DOI: 10.16108/j.issn1006-7493.2019102

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Sedimentary rocks are the main rock type that constitutes the Earth's surface. During centuries a large amount of sedimentological data have been accumulated and in the meanwhile comprehensive sedimentological databases, such as Macrostrat, have established. With the rapid growth of data in all aspects of geology including sedimentology, as well as great breakthroughs in data integration and analysis technology, it is possible to employ big-data analysis methods to explore the deep-time sedimentary process from a global perspective. The currentpaper introduces the main sedimentological databases, and analyzes their structure in detail. The innovative working mode of Macrostrat database is deciphered aiming to provide valuable experience for the sedimentological database in the Deep-time Digital Earth (DDE) Big Science Program. The database will be multi-disciplinary, multi-scaled, multi-leveled and opensource. Several study cases of employing big data analysis to solve scientific questions are also introduced here.

Analyses of Current Main Geochronological Databases and Future Perspectives

LI Qiuli, LI Yang, LIU Chunru, LU Kai, QIN Jintang, WANG Fei, WANG Tiantian, WANG Yinzhi, WU Liguang, YANG Chuan, YIN Gongming, LI Xianhua

2020, 26(1):  44.  DOI: 10.16108/j.issn1006-7493.2019100

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Geochronology, including absolute radio-isotopic dating and relative dating, is an essential subject of Earth sciences. We have seen significant advances in geochronology in the past century as demonstrated by the developments of theoretical systems, techniques, and analytical approaches, which facilitated both the quality and quantity of geochronology data. Within the suitable range of calibration, various dating methods could confirm each other, and the geochronology system becomes more accurate and explicit. For example, high-precision geochronological results and massive micro-analysis data provide time framework for geological events with unprecedented details. They lay the foundation for both quantifying the processes and rates of geological events and underpinning the coupled evolution of the Earth system. As a response to the dramatic increase of geochronological data, many databases for data management and sharing have been developed. The current paper summarizes the features of the existing geochronological databases, as well as advances in both high temporal and spatial resolution geochronology, to explore the opportunities and challenges of developing geochronology in the big data era.

Submission and Release of Petroleum Data in Australia and Their Significance

BAI Zhenrui, FENG Zhiqiang

2020, 26(1):  64.  DOI: 10.16108/j.issn1006-7493.2019096

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Petroleum geologic data is an important national asset, but its actual value relies on the level of data management. Data submission and release are important components of data management. The Australia federal government and state governments have laws and regulations on petroleum data submission and release. Among them, fines and mineral right review are the major means to restrict the submission behaviors of petroleum titleholders. The localized management makes it easy for petroleum titleholders to submit petroleum data, and a sound data management system enhances the efficiency of data management and utilization. In contrast, there are many problems in petroleum data management in China. Such problems can be solved by the following actions such as, promoting data submission through strict implementation of mineral rights management system, reducing the cost of data management through implementation of localized management, improving the current commission control system, increasing capital investment in oil and gas geological data management, as well as accelerating the establishment of the national petroleum data management system. We also suggest to improve the level of data sharing through the combination of public service and commercial service.

Paleogeographic Reconstruction Driven by Big Data: Challenges and Prospects

ZHANG Lei, ZHONG Hanting, CHEN Anqing, ZHAO Yingquan, HUANG Keke, LI Fengjie, HUANG Hu, LIU Yu, CAO Haiyang, ZHU Shengxian, MU Caineng, HOU Mingcai, JAMES G. Ogg

2020, 26(1):  73.  DOI: 10.16108/j.issn1006-7493.2019091

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Paleogeography is a typical data-reliable subject. Paleogeographic reconstruction focuses on the characteristics of geographic, life, and climate changes on the Earth's surface through geological history. In the new era of big data, the continuous accumulation of massive paleogeographic data and the rapid development of computer science technology make it possible to reconstruct the paleogeographic history using more standard and intelligent tools and software. The current paper reviews the major databases and research groups related to paleogeography, and proposes the following key components of big data-driven paleogeographic reconstruction: (1) A standard paleogeographic knowledge system; (2) An open and interactive paleogeographic data platform with new technologies such as natural-language understanding to expand data sources; (3) Paleogeographic data quality control mechanisms; (4) Various types of paleogeographic reconstruction models contructed with artificial intelligence technology; (5) Visual outputs as time-sliced maps or animations.

Construction Method and Comparison of Global Paleogeographic Reconstruction Models and Associated Knowledge Discovery

HOU Zhangshuai, FAN Junxuan, ZHANG Linna, SHEN Shuzhong

2020, 26(1):  86.  DOI: 10.16108/j.issn1006-7493.2019105

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Paleogeographic models contain reconstructions on position and motion of the tectonic plates and their surface features, and
serve as the framework for geological history reconstruction in the Deep-time Digital Earth (DDE) Big Science Program. Based on different data and methods, a lot of global paleogeographic models have been proposed in the past decades. Recently, it has become more common to use data from various fields such as paleomagnetism, paleontology, sedimentology, geophysics, geochemistry, and geodynamics to establish a digital, detail-adjustable, and self-evolving model. The current paper reviews the existing methods of global paleogeographic reconstruction models, and conducts a comprehensive comparison of the six main reconstruction models (PaleoMap, PLATES, UNIL, GOLONKA, GMAP, and EarthByte), as a reference for domestic research. We also introduced the applications and knowledge discoveries of reconstructed paleogeographic models in the field of paleoclimate, plate tectonics, and basin evolution. Under the framework of DDE program, a unified four-dimensional paleogeographic model will be reconstructed with efforts from paleogeographic scientists all over the world.


Current Status of Geological Map Database and Future Perspectives of Geological Mapping

YANG Xingchen, WU Zhenhan, ZHANG Sumei, GAO Xi, Han Lele, DING Weicui, YANG Yan, ZHANG Yu, YE Mengni, YANG Yaqi

2020, 26(1):  100.  DOI: 10.16108/j.issn1006-7493.2019101

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Geological map is one of the most important carriers of geological information. It contains the condensed discoveries of geological research and understandings of geological process. In the new era of big data, the guiding theories and methods for geological mapping have been thoroughly altered. Scientists around the world have used the new methods and techniques to establish a series ofexcellent databases related to geological maps, such as OneGeology, OpenGeoscience, NGMDB, and Geological Cloud. The effective operation of these databases provides a great amount of geoscience data and convenient information services for geologists worldwide. The current paper reviews the existing databases related to geological map and their operations., This could provide experience for integrating global databases related to geological map and constructing data platforms in Deep-Time Digital Earth (DDE) Big Science Program. History of geological mapping is reviewed and the techniques and common software serving for geological mapping are introduced. In order to meet the present needs of economic society about geological information service, as well as the mission of DDE, necessary actions such as strengthening the international cooperation of geological mapping, introducing AI technique, and enhancing data sharing, are proposed.

Recent Advances in the Data-driven Play Fairway Analysis for Geothermal Exploration

JIANG Shu, WANG Shuai, QI Shihua, CHENG Wanqiang, KUANG Jian, HUANG Xuelian, TIAN Feng, XIAO Zhicai

2020, 26(1):  111.  DOI: 10.16108/j.issn1006-7493.2019098

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Geological, geophysical and geochemical data, as well as data from other related areas, are key elements to predict a geothermal system. Heat source, migration pathways for fluids or heat, geothermal reservoir, cap rock, and heat preservation conditions are all critical for the formation and preservation of a working geothermal system. At the same time, the topography, seismicity, wildlife protection, environmentally sensitive area and infrastructure all should be evaluated for geothermal exploration. Our survey on geothermal databases showed that the historically accumulated data have been organized in different geothermal databases under different formats. These databases belong to only a few countries and as a result accumulate data mainly from their area. Large amount of data from other regions is still scattered and in an unorganized condition. Geographic Information System tools and mathematical methods might be helpful to solve this problem. Play fairway analysis with fuzzy logic technique or machine learning approach could be used to analyse the different types of data, and then predict the hidden geothermal system.