生成式AI技术在地学研究中的应用现状及发展趋势

doi:10.13745/j.esf.sf.2025.4.68

地学前缘 ›› 2025, Vol. 32 ›› Issue (4): 303-316.DOI: 10.13745/j.esf.sf.2025.4.68

• 人工智能驱动地学知识发现 • 上一篇下一篇

生成式AI技术在地学研究中的应用现状及发展趋势

周圣荃¹(), 李以科¹^,^*(), 王永志²^,³^,⁵, 刘海明¹, 李楠¹, 柯昌辉¹, 李瑞萍¹, 赵永岗⁴, 张丽⁴

1.中国地质科学院矿产资源研究所, 北京 100037
2.吉林大学综合信息矿产预测研究所, 吉林长春 130061
3.吉林大学地球探测科学与技术学院, 吉林长春 130061
4.包头钢铁(集团)有限责任公司, 内蒙古包头 014010
5.新疆维吾尔自治区地质研究院, 新疆乌鲁木齐 830057

收稿日期:2025-01-15 修回日期:2025-05-23 出版日期:2025-07-25 发布日期:2025-08-04
通信作者: *李以科(1983—),男,研究员,主要从事战略性关键矿产成矿作用与找矿预测研究。E-mail: like430@cags.ac.cn
作者简介:周圣荃(2001—),男,博士研究生,主要从事地球物理探测与矿产勘查技术研究。E-mail: 1227362016@qq.com
基金资助:
国家重点研发计划项目(2024YFC2909904);国家自然科学基金项目(42072114);国家自然科学基金项目(42230810);中国地质调查局地质调查项目(DD20230366);中国地质调查局地质调查项目(DD202211695);包头钢铁(集团)有限责任公司科技成果转化项目(HE2332);包头钢铁(集团)有限责任公司科技成果转化项目(HE2228);包头钢铁(集团)有限责任公司科技成果转化项目(HE2313)

Current status and development trends of generative AI technology in Earth science research

ZHOU Shengquan¹(), LI Yike¹^,^*(), WANG Yongzhi²^,³^,⁵, LIU Haiming¹, LI Nan¹, KE Changhui¹, LI Ruiping¹, ZHAO Yonggang⁴, ZHANG Li⁴

1. Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
2. Institute of Integrated Information for Mineral Resources Prediction, Jilin University, Changchun 130061, China
3. College of Geoexploration Science and Technology, Jilin University, Changchun 130061, China
4. Bayan Obo Iron Mine, Baotou Iron and Steel Group Co., Ltd., Baotou 014010, China
5. Xinjiang Academy of Geological Research, ürümqi 830057, China

Received:2025-01-15 Revised:2025-05-23 Online:2025-07-25 Published:2025-08-04

摘要/Abstract

摘要：

地球复杂巨系统研究的多维度发展持续驱动着地学研究方法的突破创新。生成式AI技术作为新兴研究工具,凭借强大的数据处理与知识推理能力为地学研究提供了新的思路。本文系统梳理生成式AI技术的发展脉络,对比ChatGPT与国产DeepSeek模型的技术路线,展现了生成式AI技术强大的自然语言处理能力和多模态模型构建的优势。本文详细总结了生成式AI技术在地学中的应用,特别是在资源勘查领域的应用现状和发展趋势。生成式AI技术已实现地学领域中多源异构数据的通用性整合,在资源勘查领域深入参与各阶段工作并于数据整合、认知推理、应用服务等层面提供技术支撑,掀起资源勘查领域革新浪潮。目前,生成式AI技术在地学应用领域依然存在数据完备性缺陷、地质复杂系统挑战、地质模型可解释性难题等核心制约。综合分析指出,生成式AI重构“数据感知-知识提炼-决策生成”技术体系,必将加速实现该技术在地球科学各领域的应用突破,为创新勘查技术方法、提高勘查效率、助力新一轮找矿突破战略行动、保障国家能源安全提供重要技术支撑。

关键词: 人工智能, 生成式AI技术, ChatGPT, 找矿预测, 资源勘查, DeepSeek

Abstract:

The multidimensional development of Earth’s complex giant system research continues to drive the breakthrough innovation of geoscientific research methods. As an emerging research tool, generative AI technology provides new ideas for geoscientific research by virtue of its powerful data processing and knowledge reasoning capabilities. In this paper, we systematically sort out the development of generative AI technology, compare the technical routes of ChatGPT and the domestic DeepSeek model, and show the advantages of generative AI technology’s powerful natural language processing capability and multimodal model construction. This paper summarizes in detail the application of generative AI technology in geosciences, especially the current status and development trend of its application in the field of resource exploration. Generative AI technology has realized the universal integration of heterogeneous data from multiple sources in the field of geosciences, and has been deeply involved in various stages of work in the field of resource exploration and provided technical support at the levels of data integration, cognitive reasoning, application services, etc., which has set off a wave of innovation in the field of resource exploration. At present, generative AI technology in the field of geoscience applications still exists in data completeness defects, geological complex system challenges, geological model interpretability problems and other core constraints. Comprehensive analysis points out that generative AI reconstructs the technical system of “data perception-knowledge refinement-decision generation”, which will certainly accelerate the realization of breakthroughs in the application of this technology in the fields of Earth science, and provide important technical support for the innovation of exploration technology and methodology, improvement of exploration efficiency, and assisting the new round of strategic action of finding mineral breakthroughs to safeguard the country’s energy security.

Key words: artificial intelligence, generative AI technology, ChatGPT, ore prospecting, resource exploration, DeepSeek

中图分类号:

P628.1

周圣荃, 李以科, 王永志, 刘海明, 李楠, 柯昌辉, 李瑞萍, 赵永岗, 张丽. 生成式AI技术在地学研究中的应用现状及发展趋势[J]. 地学前缘, 2025, 32(4): 303-316.

ZHOU Shengquan, LI Yike, WANG Yongzhi, LIU Haiming, LI Nan, KE Changhui, LI Ruiping, ZHAO Yonggang, ZHANG Li. Current status and development trends of generative AI technology in Earth science research[J]. Earth Science Frontiers, 2025, 32(4): 303-316.

图/表 8

表1 典型生成式AI技术大语言模型

Table 1 Typical generative AI technology large language model

模型名	时间	参数量	研发公司
GPT-3	2020年5月	1 750亿	OpenAI
FLAN	2021年9月	1 370亿	Google
BERT	2019年10月	4 810亿	Google
PaLM	2022年4月	5 400亿	Google
LLaMA	2023年2月	650亿	Facebook
GPT-4	2023年3月	未知	OpenAI
文心一言	2023年4月	未知	百度
MinMax	2023年5月	未知	商汤科技
Gemma7B	2024年2月	93亿	Google
Kimi	2024年10月	2 000亿	Moonshot AI
DeepSeek-R1	2025年1月	6 710亿	深度求索

表2 ChatGPT与DeepSeek模型对比分析表

Table 2 Comparative analysis of ChatGPT and DeepSeek model

对比角度	ChatGPT (GPT-4架构)	DeepSeek (V1架构)
技术架构	密集参数模型	混合专家模型(MoE)
参数量	1.8万亿(估算)	6 850亿
训练成本	>1亿美元	约560万美元
训练方法	三阶段训练(预训练+SFT+RLHF)	五阶段训练(冷启动+推理RL+MoE+蒸馏+场景RL)
推理速度	80 tokens/s	200 tokens/s
样本截止时间	2023年10月	实时更新(周粒度)
核心优势	跨领域泛化能力多模态支持成熟应用程序接口生态	支持本体库嵌入动态计算能耗降多领域数据生成
主要局限	硬件依赖性强知识更新延迟	多模态支持有限领域迁移成本高
典型应用场景	通用对话/跨领域问答/代码生成	专业领域/智能问答/方案优化
部署方案	云端应用程序接口服务	私有化部署/混合云架构

图1 ChatGPT地震类型划分操作流程

Fig.1 ChatGPT earthquake type classification operation process

图2 ChatClimate 数据管道:从创建外部存储器、接收问题到来自 IPCC AR6 的准确答案(引自文献[34])

Fig.2 ChatClimate data pipeline: from creating external memory, receiving questions to accurate answers from IPCC AR6. Adapted from [34].

图3 长期、复杂的月球驻留科学探测与资源开发利用系统工程体系(引自文献[37])

Fig.3 Engineering system of lunar resident scientific exploration and resources development and utilization system. Adapted from [37].

图4 基于机器学习技术的矿物地球化学数据获取和分析流程(修改自文献[92]) (a)—取样和样品制备图;(b)—数据分析和数据缩减图。

Fig.4 Mineral geochemical data acquisition and analysis process based on machine learning technology. Modified after [92].

图5 基于机器学习技术的矿物数据挖掘工作流程(修改自文献[92])

Fig.5 Mineral data mining workflow based on machine learning technology. Modified after [92].

图6 资源勘查领域典型专业大模型架构图

Fig.6 Architecture diagram of typical professional big model in the field of resource exploration

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生成式AI技术在地学研究中的应用现状及发展趋势

Current status and development trends of generative AI technology in Earth science research

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