划分火成岩构造背景的新方法:机器学习和 GeoTectAI 软件

IF 2.7 4区 地球科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Ming Lei, Wenyan Cai, Xiao Liu, Chao Zhang, Qingyi Cui, Jian Li
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引用次数: 0

摘要

长期以来,阐明未知岩石样本的构造背景一直是地质学家关注的焦点。传统的方法因其固有的局限性而受到越来越多的质疑。为了应对这些挑战,本文应用现代机器学习技术来分析火成岩的地球化学数据,以提高对构造环境的理解。本文采用了多种机器学习模型,包括决策树、K-近邻、支持向量机、随机森林、极梯度提升和人工神经网络,并使用由九种主要元素(SiO2、TiO2、Al2O3、CaO、MgO、MnO、Na2O、K2O 和 P2O5)以及 14 种微量元素(La、Ce、Pr、Nd、Sm、Eu、Gd、Tb、Dy、Ho、Er、Tm、Yb 和 Lu)组成的 23 个特征进行训练,研究成功地区分了七种不同的构造环境。在这些模型中,随机森林(Random Forest)、极端梯度提升(Extreme Gradient Boosting)和人工神经网络(Artificial Neural Networks)的分类准确率和召回率都很高,分别为 0.85、0.87 和 0.86。这验证了机器学习技术在通过地球化学元素区分火成岩构造环境方面的有效性和潜力。为了使地质学家和研究人员无需掌握机器学习知识就能更准确地理解和预测火成岩的成因,我们开发了一款用户友好型软件--GeoTectAI。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

A new approach to dividing the tectonic setting of igneous rocks: machine learning and GeoTectAI software

A new approach to dividing the tectonic setting of igneous rocks: machine learning and GeoTectAI software

For a long time, elucidating the tectonic setting of unknown rock samples has been a focal point for geologists. Traditional methodologies for this purpose have been scrutinized increasingly due to their inherent limitations. In response to these challenges, this paper applies modern machine learning techniques to analyze the geochemical data of igneous rocks and improve understanding of tectonic settings. By employing a variety of machine learning models, including Decision Trees, K-Nearest Neighbors, Support Vector Machines, Random Forests, Extreme Gradient Boosting, and Artificial Neural Networks, and training with 23 features comprising nine major elements (SiO2, TiO2, Al2O3, CaO, MgO, MnO, Na2O, K2O, and P2O5) along with 14 trace elements (La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), the study successfully distinguished between seven different tectonic settings. Among these models, Random Forest, Extreme Gradient Boosting, and Artificial Neural Networks demonstrated superior classification accuracy and recall rates, with accuracies of 0.85, 0.87, and 0.86, respectively. This validates the effectiveness and potential of machine learning technologies in distinguishing the tectonic settings of igneous rocks through their geochemical elements. To enable geologists and researchers to more accurately understand and predict the origins of igneous rocks without the need to master machine learning knowledge, a user-friendly software, GeoTectAI, has been developed.

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来源期刊
Earth Science Informatics
Earth Science Informatics COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-GEOSCIENCES, MULTIDISCIPLINARY
CiteScore
4.60
自引率
3.60%
发文量
157
审稿时长
4.3 months
期刊介绍: The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.
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