大数据时代的地热学研究思考及展望

旷健; 祁士华; 蒋恕; 姚宏

doi:10.3799/dqkx.2021.144

大数据时代的地热学研究思考及展望

doi: 10.3799/dqkx.2021.144

旷健^1, ,,
祁士华^1, ,,
蒋恕²,
姚宏³

1.
中国地质大学生物地质与环境地质国家重点实验室, 湖北武汉 430078
2.
中国地质大学资源学院, 湖北武汉 430074
3.
中国地质大学计算机学院, 湖北武汉 430078

基金项目:

中国地质大学（武汉）DDE专项经费 G1323520325

详细信息

作者简介:
旷健（1995-），男，博士研究生，主要从事地热学研究. ORCID：0000-0002-3783-7790. E-mail：kuangjian@cug.edu.cn

通讯作者:
祁士华, E-mail: shihuaqi@cug.edu.cn

中图分类号: P314; P547
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出版历程
- 收稿日期: 2021-07-12
- 网络出版日期: 2023-10-07
- 刊出日期: 2023-09-25

Thinkings and Prospects for the Research on Geothermics in the Big Data Era

Kuang Jian^{1
,
,},
Qi Shihua^{1
, ,},
Jiang Shu²,
Yao Hong³

1.
State Key Laboratory of Biogeology and Environmental Geology, China University of Geosciences, Wuhan 430078, China
2.
School of Earth Resources, China University of Geosciences, Wuhan 430074, China
3.
School of Computer Science, China University of Geosciences, Wuhan 430078, China

摘要

摘要: 大数据时代带来产业、思想和科学的革命，数据的融合、归纳、科学发现等对地热学的发展带来了机遇和挑战.地热学作为一门多学科交叉融合学科，涉及到地球科学的方方面面，其在大数据时代下的发展也面临着诸多挑战.本文依据时间序列将地热学划分为当代地热学和历史地热学两部分，从中国乃至全球热流分布、水化学参数、地热潜力评价体系3个方面对当代地热学涉及的地球热结构及相关能源灾害问题，以及从深时地球热体制变化、构造演化、气候环境能源效应3个方面对历史地热学涉及的热时空变化、演变及资源形成分别阐述了大数据引入的前瞻性和可行性.未来地热学的发展一方面应注重地热学框架和要素的搭建及相关科学联系解译和科学发现工作，另一方面则应注重地热学知识图谱构建，多学科知识融合解译，继而重建深时地球热状态及其所反映的物理化学过程.
- 大数据 /
- 地热学 /
- 科学前沿 /
- 深时地球 /
- 学科发展 /
- 地热勘查
Abstract: The era of big data drives the revolution of industry, thoughts, and science. The integration, induction, and scientific discovery of data bring opportunities and challenges to the development of geothermics. As a multi-disciplinary or interdisciplinary subject, geothermics involves all aspects of earth science, which poses challenges to the extremely complex and difficult development of geothermics in the era of big data. In this study, we divide geothermics into contemporary geothermics and historical geothermics based on time. First, we analyze the geothermal structure and related energy/disasters from three aspects of heat flow distribution in China and the world, hydrochemical parameters, and geothermal potential evaluation system. Second, we discuss the temporal and spatial changes of heat, the tectonic evolution, and the effect of climate, environment, and energy from three aspects of temporal and spatial variation, evolution, and resource formation of heat in historical geothermics. All these efforts aim to expound the foresight and feasibility of big data introduction, respectively. In the future, the development of geothermics should focus on the construction of the framework and elements of geothermics, the interpretation of related scientific links, and discovery. Meanwhile, it should focus on the construction of the knowledge graph of geothermics, the integration, interpretation, and discovery of multi-disciplinary knowledge, and the reconstruction of the deep-time geothermal state and physical and the chemical processes reflected.
- big data /
- geothermics /
- scientific frontier /
- deep-time earth /
- discipline development /
- geothermal prospecting

HTML全文

图 1 全球热流测点分布（据Lucazeau, 2019）

Fig. 1. Distribution of global heat flow measuring points (from Lucazeau, 2019)

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图 2 中国陆区热流测点分布

图中数据来源Jiang et al.（2019）；底图改自中国标准地图GS（2019）1824号

Fig. 2. Distribution of heat flow measuring points in China

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图 3 中国热水分布

图修改自陈墨香（1992）、Jiang et al.（2019）；底图改自中国标准地图GS（2016）1603和GS（2016）1608

Fig. 3. Distribution of hot spring in China

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图 4 哥斯达黎加（a）、印度尼西亚（b）、新西兰（c）、中国羊八井（d）典型的岩浆型高温地热系统水化学数据

图a数据量N=98，数据来源Giggenbach and Soto（1992）、Gherardi et al.（2002）、Marini et al.（2003）、Molina and Martí（2016）；图b数据量N=8，数据来源Hochstein and Sudarman（1993）；图c数据量N=69，数据来源Giggenbach et al.（1994）、Glover and Mroczek（2009）；图d数据量N=33，数据来源Guo et al.（2008）、Zhang et al.（2015）

Fig. 4. Hydrochemical data of typical magmatic high temperature geothermal systems in Costa Rica (a), Indonesia (b), New Zealand (c) and Yangbajing of China (d)

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图 5 美国黄石公园、日本东北部火山区、哥斯达黎加火山区、印度尼西亚爪哇火山区等178件水化学数据

数据来源Kiyosu（1985）、Lewis et al.（1997，1998）、Takahashi et al.（2000）、Delmelle et al.（2000）、Nanlohy et al.（2001）、Marini et al.（2003）、Nordstrom et al.（2009）、Deng et al.（2011）；a. 地热水中氯离子浓度和硫酸根浓度分别与地热水pH关系，显示出Cl^-和SO₄^2‒离子与pH具有明显地负相关关系，pH值和lg（SO₄^2‒）呈线性关系；b. 地热水中主要阴阳离子浓度关系，显示出氯离子和钠离子分别是地热水中阴阳离子的主要组成部分

Fig. 5. 178 hydrochemical data were collected from Yellowstone Park, northeastern Japan, Costa Rica and Java, Indonesia

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图 6 全球变质岩变质T/P随时间变化图（改自Weller and St-Onge, 2017）

Fig. 6. Time-dependent change of T/P of global metamorphic rocks (modified by Weller and St-Onge, 2017)

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图 7 全球变质岩变质高中低T/P随时间变化（改自Brown and Johnson, 2019）

Fig. 7. Time-dependent change of high, medium, low T/P of global metamorphic rocks (modified by Brown and Johnson, 2019)

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图 8 “双峰式”变质作用随时间演化的规律（改自Holder et al., 2019）

Fig. 8. Time-dependent change of bimodal metamorphism (modified by Holder et al., 2019)

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图 9 新生代印度‒欧亚大陆形成示意

a. 新生代变质岩岩石变质温压比，数据源于Brown and Johnson（2019）；黑色实线为全球板块边界，数据源于Bird（2003）；b. 70 Ma印度大陆北漂示意，改自van Hinsbergen et al.（2021）

Fig. 9. The formation of Cenozoic India-Eurasia continent

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图 10 全球斑岩型铜矿分布

红点为斑岩型铜矿，数据源于Mutschler et al.（2000）；黑色实线为全球板块边界，数据源于Bird（2003）；大部分斑岩矿床分布在3个成矿域内，即环太平洋、特提斯和中亚成矿域

Fig. 10. Distribution of porphyry copper deposits in the world

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图 11 大数据下地热学领域研究框架结构

Fig. 11. The framework structure of research in the geothermic with big data

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图 12 地热大数据未来工作路线

Fig. 12. Roadmap for future work on geothermal big data

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