基于锆石痕量元素的机器学习在成矿潜力特征描述中的应用:特提安星域案例研究

IF 2.2 4区 地球科学 Q2 GEOCHEMISTRY & GEOPHYSICS Minerals Pub Date : 2024-09-16 DOI:10.3390/min14090945
Jin Guo, Wen-Yan He
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引用次数: 0

摘要

摘要:在人工智能和信息技术突飞猛进的今天,大数据和机器学习的出现为矿产勘查提供了新的研究范式。本文以哲金成矿领域为研究对象,基于机器学习方法开展了一系列研究工作,探索影响斑岩矿床成矿潜力的关键地球化学元素信号,揭示成矿规律性。建立了基于随机森林、XGBoost 和深度神经网络的二元分类器来区分锆石肥度,与传统地球化学方法相比,这些机器学习方法达到了更高的准确率,超过 90%。基于随机森林和 SHapley Additive exPlanations(SHAP)算法,揭示了有利于岩浆成矿的关键化学元素特征。此外,还构建了矿床分类模型,并利用 t-SNE 方法直观地显示了不同成矿类型斑岩矿床之间锆石痕量元素特征的差异。该研究通过将机器学习算法与传统的化学方法进行比较,强调了机器学习算法在成矿潜力评估和矿产勘探方面的前景,为未来利用亚矿物地球化学数据建立矿藏分类模型提供了启示。
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Application of Machine Learning to Characterize Metallogenic Potential Based on Trace Elements of Zircon: A Case Study of the Tethyan Domain
Abstract: Amidst the rapid advancement of artificial intelligence and information technology, the emergence of big data and machine learning provides a new research paradigm for mineral exploration. Focusing on the Tethyan metallogenic domain, this paper conducted a series of research works based on machine learning methods to explore the critical geochemical element signals that affect the metallogenic potential of porphyry deposits and reveal the metallogenic regularity. Binary classifiers based on random forest, XGBoost, and deep neural network are established to distinguish zircon fertility, and these machine learning methods achieve higher accuracy, exceeding 90%, compared with the traditional geochemical methods. Based on the random forest and SHapley Additive exPlanations (SHAP) algorithms, key chemical element characteristics conducive to magmatic mineralization are revealed. In addition, a deposit classification model was constructed, and the t-SNE method was used to visualize the differences in zircon trace element characteristics between porphyry deposits of different mineralization types. The study highlights the promise of machine learning algorithms in metallogenic potential assessment and mineral exploration by comparing them with traditional chemical methods, providing insights into future mineral classification models utilizing sub-mineral geochemical data.
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来源期刊
Minerals
Minerals MINERALOGY-MINING & MINERAL PROCESSING
CiteScore
4.10
自引率
20.00%
发文量
1351
审稿时长
19.04 days
期刊介绍: Minerals (ISSN 2075-163X) is an international open access journal that covers the broad field of mineralogy, economic mineral resources, mineral exploration, innovative mining techniques and advances in mineral processing. It publishes reviews, regular research papers and short notes. Our aim is to encourage scientists to publish their experimental and theoretical results in as much detail as possible. There is no restriction on the length of the papers. The full experimental details must be provided so that the results can be reproduced.
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