大数据人工智能驱动的矿产预测

左仁广; 张振杰; 杨帆帆; 许莹; 熊义辉; 王子烨

doi:10.3799/dqkx.2026.006

大数据人工智能驱动的矿产预测

doi: 10.3799/dqkx.2026.006

1.
中国地质大学(武汉)地质过程与成矿预测全国重点实验室, 湖北武汉 430078
2.
中国地质大学(北京)地质过程与成矿预测全国重点实验室, 北京 100083

基金项目:

国家自然科学基金项目 42425208

国家自然科学基金项目 42530801

详细信息

作者简介:
左仁广（1981—），男，教授，博士生导师，主要从事数学地球科学与智能矿产预测研究. ORCID：0000-0002-5639-3128. E-mail：zrguang@cug.edu.cn

中图分类号: P628
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- PDF下载量: 151
- 被引次数: 0
出版历程
- 收稿日期: 2025-12-29
- 刊出日期: 2026-03-25

Big Data and Artificial Intelligence-Driven Mineral Prospectivity Mapping

1.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (Wuhan), Wuhan 430078, China
2.
State Key Laboratory of Geological Processes and Mineral Resources, China University of Geosciences (Beijing), Beijing 100083, China

摘要

摘要:
大数据和人工智能时代未来已来.大数据是一种解决问题的新思维，强调变量间的多维关联性，让数据“发声”，从而得到新的洞察和启发性的答案.人工智能是一种新的数据挖掘方法，具有强大的非线性建模能力，可深度挖掘数据，发现隐蔽规律.大数据人工智能驱动的矿产预测已经成为全球矿业科技竞争的制高点，正在重塑矿产勘查的研究范式.本文提出了大数据人工智能驱动的矿产预测的基本概念和主要组成，分析了智能认知、智能学习和智能决策的科学内涵、研究进展和关键科学与技术问题，指出三者是智能矿产预测的重要组成部分，分别对应实现地球系统与成矿系统、成矿系统与勘查系统，以及勘查系统与评价系统的关联.未来，大数据人工智能驱动的矿产预测要重视找矿大数据构建、地质约束矿产预测人工智能新算法、图像处理大算力、创新性复合人才培养等方面.
- 大数据 /
- 人工智能 /
- 矿产预测 /
- 智能认知 /
- 智能学习 /
- 智能决策
Abstract:
We have entered the era of big data and artificial intelligence. Big data or big data analytics is a new thinking for solving geoscientific problems, emphasizing the multidimensional associations among variables and allowing data to speak for themselves, thereby leading to new insights and more informative answers. Artificial intelligence is a new data mining approach with a strong non-linear modeling ability, which can deeply mine data and discover hidden patterns. The big data and artificial intelligence-driven mineral prospectivity mapping has become the high ground of global mining technology competition, reshaping the paradigm of mineral exploration. This study proposes the basic concepts and main components of big data and artificial intelligence-driven mineral prospectivity mapping, analyzes the scientific connotations, the state-of-the-art, and key scientific and technological issues in intelligent cognition, intelligent learning, and intelligent decision-making. These three key parts are essential components of intelligent mineral prospectivity mapping, and link between the Earth system and mineral system, mineral system and exploration system, and exploration system and evaluation system, respectively. In the future, big data and artificial intelligence-driven mineral prospectivity mapping should focus on the construction of prospecting big data, new geological constrained algorithms for mineral prospectivity mapping, high-performance computing in image processing, and the cultivation of innovative interdisciplinary talent.
- big data /
- artificial intelligence /
- mineral prospectivity mapping /
- intelligent cognition /
- intelligent learning /
- intelligent decision-making

HTML全文

图 1 矿产预测工作流程

矿床模型修改自孟旭阳和毛景文（2025）

Fig. 1. A workflow of mineral prospectivity mapping

下载: 全尺寸图片幻灯片

图 2 矿产预测系统工程

成矿系统和勘查系统分别修改自孟旭阳和毛景文（2025）和成秋明（2025）

Fig. 2. A systems engineering of mineral prospectivity mapping

下载: 全尺寸图片幻灯片

图 3 大数据人工智能驱动的矿产预测

Fig. 3. Big data and artificial intelligence-driven mineral prospectivity mapping

下载: 全尺寸图片幻灯片

图 4 智能认知工作流程

Fig. 4. A workflow of intelligent cognition

下载: 全尺寸图片幻灯片

图 5 智能学习工作流程

Fig. 5. A workflow of intelligent learning

下载: 全尺寸图片幻灯片

图 6 智能决策工作流程

Fig. 6. A workflow of intelligent decision-making

下载: 全尺寸图片幻灯片

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