Construction of Multilayered Mineralization Prediction Data Based on Geological Big Data and Artificial Intelligence
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摘要:
随着大数据和人工智能技术的迅猛发展，矿产预测正在经历一场由数据稀疏型向数据密集型的技术变革，成为矿产勘查和找矿突破的重要“科技引擎”.尽管人们在地质信息化的进程中已经积累了大量的地质矿产、地球物理、地球化学和遥感等多源异构工程探测数据，以及丰富的地质报告和文献资源，但如何高效整合并深度挖掘这些数据，以进一步优化矿产预测指标体系、构建高质量矿产预测数据集、提升预测精度，仍是当前研究亟待解决的关键问题.针对这些挑战，本文提出利用大模型和知识图谱技术，整合地球系统、成矿系统、勘查系统与预测评价系统的多层次、多维度知识信息，打造多系统耦合的矿产预测知识图谱，实现矿产预测指标体系的智能化构建.同时，基于大数据和人工智能技术，形成以地学探测数据智能挖掘、科学数据智能抽取和时空分析，以及数据智能反演和模拟为核心的多层次矿产预测数据体系.这一方法体系通过推动预测数据与预测指标的深度耦合，可有效提升预测结果的准确性和可靠性，为矿产勘查和找矿突破提供强有力的技术支持.
Abstract:With the advancement of big data and artificial intelligence technologies, mineralization prediction is undergoing a series of technological innovations, transitioning from data-sparse to data-intensive approaches. This shift is expected to become a new "technology engine" for breakthroughs in mineral exploration and discovery. Despite the substantial accumulation of heterogeneous multisource geological, geophysical, geochemical, and remote sensing data, as well as rich geological reports and literature, it remains a critical research challenge to effectively integrate and deeply mine these valuable data resources to further optimize mineralization prediction indicator systems and construct high-quality mineralization prediction datasets. To address the challenge, this paper proposes integrating multilayered and multidimensional mineralization prediction knowledge across the Earth system, metallogenic system, exploration system, and prediction-evaluation system through large models and knowledge graph technologies. A multilayered, multi-system-coupled mineralization prediction knowledge graph will be constructed, and intelligent construction of mineralization prediction indicator systems will be achieved through knowledge graph mining. Based on big data and artificial intelligence technologies, a multilayered method system for intelligent construction of mineralization prediction data will be developed. This system will focus on intelligent mining of geological exploration data, automated scientific data extraction and spatiotemporal analysis, and intelligent data inversion and simulation. Such advancements are expected to strengthen the deep coupling between prediction data and indicators, enhancing the accuracy and reliability of prediction results, and providing more robust technical support for mineral exploration and discovery breakthroughs.
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图 1 基于知识图谱的矿产预测模型自动构建流程
Fig. 1. Automated workflow for mineral prospectivity modeling based on knowledge graphs
图 2 基于人工智能和大数据的多层次矿产预测数据智能构建方法
Fig. 2. Multi-level intelligent data construction method for mineral prospectivity prediction based on artificial intelligence and big data
图 3 “地球系统‒成矿系统‒勘查系统‒预测评价系统”耦合的矿产预测流程
Fig. 3. Coupled metallogenic prediction workflow integrating the "Earth system-metallogenic system-exploration system-prediction and evaluation system"
图 4 矿产预测知识图谱示意图
Fig. 4. Schematic diagram of the knowledge graph for mineral prospectivity prediction
图 5 矿产预测知识图谱构建流程
Fig. 5. Workflow for constructing the knowledge graph of mineral prospectivity prediction.
图 6 小比例地球化学数据与高分辨率遥感图像融合（据Wang et al., 2021）
Fig. 6. Integration of low-scale geochemical data and high-resolution remote sensing imagery (modified by Wang et al., 2021)
图 7 西藏冈底斯地区新生代以来火成岩Nd-Hf-O同位素填图（据Hou et al., 2023数据重绘）
Fig. 7. Mapping of Nd-Hf-O isotopes in Cenozoic igneous rocks of the Gangdese region, Xizang (modified by Hou et al., 2023)
图 8 粤北凡口铅锌矿成矿力‒热‒流场模拟
图据Xiao et al.（2024）；a.钒口铅锌矿勘查剖面图；b.冯•米塞斯应力；c.第一主应力；d.第二主应力；e.第三主应力；f.流体通量模拟；g.体积应变；h.第一主应变；i.第二主应变；j.第三主应变；k.热力场模拟；l.铅锌矿随机森林预测结果
Fig. 8. Simulation of structural stress, strain, thermal, and fluid fields for the Fankou Pb-Zn deposit, northern Guangdong
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