地学前缘 ›› 2022, Vol. 29 ›› Issue (5): 464-475.DOI: 10.13745/j.esf.sf.2022.2.75
朱紫怡1(), 周飞1, 王瑀1, 周统1, 侯照亮2, 邱昆峰1,3,*()
收稿日期:
2021-12-16
修回日期:
2022-03-18
出版日期:
2022-09-25
发布日期:
2022-08-24
通讯作者:
邱昆峰
作者简介:
朱紫怡(2000—),女,本科生,地质学专业。E-mail: ziyizhuuu@qq.com
基金资助:
ZHU Ziyi1(), ZHOU Fei1, WANG Yu1, ZHOU Tong1, HOU Zhaoliang2, QIU Kunfeng1,3,*()
Received:
2021-12-16
Revised:
2022-03-18
Online:
2022-09-25
Published:
2022-08-24
Contact:
QIU Kunfeng
摘要:
锆石是在自然界中多种温压条件下能够稳定保存,并记录原岩年龄信息的副矿物。锆石微量元素能完整记录地质演化过程信息。通过微量元素分析锆石成因的研究已久,通常利用Th-U图解和LaN-(Sm/La)N图解等二元图解对锆石进行分类研究。然而,随着锆石研究的深入,以及二元图解无法呈现数据高维度信息的局限性,传统图解已经不能满足对锆石类型进行准确判别,且对已知类型的锆石出现判定偏差。因此,本文将地质大数据与机器学习相结合,训练出高维度锆石成因分类器。文中收集了3 498条不同成因类型的锆石微量元素数据,并通过测试和运用随机森林、支持向量机、人工神经网络和k近邻等4种机器学习算法,最终得出准确率为86.8%的线性支持向量机锆石成因分类器,用于锆石类型的判定与预测。这项工作为锆石分类研究提供了更高维度的判别手段,极大提高了微量元素分析成因结果的精度。将锆石微量元素数据与机器学习方法相结合,是大数据分析与机器学习技术在地球化学研究中的积极探索。
中图分类号:
朱紫怡, 周飞, 王瑀, 周统, 侯照亮, 邱昆峰. 基于机器学习的锆石成因分类研究[J]. 地学前缘, 2022, 29(5): 464-475.
ZHU Ziyi, ZHOU Fei, WANG Yu, ZHOU Tong, HOU Zhaoliang, QIU Kunfeng. Machine learning-based approach for zircon classification and genesis determination[J]. Earth Science Frontiers, 2022, 29(5): 464-475.
图1 不同类型的锆石微量元素散点图(底图区域据文献[9])
Fig.1 Scatter plot of the published zircon-associated trace elements from various rock types. Background region based on [9].
判别图解 | 准确率/% | |||
---|---|---|---|---|
岩浆锆石 | 热液锆石 | 变质锆石 | 平均 | |
Th/U | 81.65 | - | 23.5 | 73.1 |
LaN-(Sm/La)N | 42.4 | 20.9 | - | 38.5 |
表1 已发表的锆石成因类型判别图解准确率
Table 1 The accuracy of the published diagram to classify different zircon provenance
判别图解 | 准确率/% | |||
---|---|---|---|---|
岩浆锆石 | 热液锆石 | 变质锆石 | 平均 | |
Th/U | 81.65 | - | 23.5 | 73.1 |
LaN-(Sm/La)N | 42.4 | 20.9 | - | 38.5 |
序号 | 母岩类型 | 数据量 | 参考文献 |
---|---|---|---|
1 | 岩浆锆石 | 2466 | [ |
2 | 热液锆石 | 508 | [ |
3 | 变质锆石 | 524 | [ |
表2 不同母岩类型锆石微量元素数据量
Table 2 Published data of zircon trace elements from different provenance
序号 | 母岩类型 | 数据量 | 参考文献 |
---|---|---|---|
1 | 岩浆锆石 | 2466 | [ |
2 | 热液锆石 | 508 | [ |
3 | 变质锆石 | 524 | [ |
元素 | 元素数据缺失率 | 元素 | 元素数据缺失率 | ||||
---|---|---|---|---|---|---|---|
热液 锆石 | 岩浆 锆石 | 变质 锆石 | 热液 锆石 | 岩浆 锆石 | 变质 锆石 | ||
La | 0% | 7% | 23% | Tm | 4% | 1% | 17% |
Ce | 0% | 0% | 0% | Yb | 0% | 0% | 0% |
Pr | 0% | 1% | 4% | Lu | 0% | 0% | 12% |
Nd | 0% | 0% | 2% | Y | 20% | 7% | 38% |
Sm | 0% | 0% | 1% | Hf | 27% | 8% | 41% |
Eu | 1% | 0% | 1% | Nb | 38% | 23% | 58% |
Gd | 0% | 0% | 0% | Ta | 36% | 67% | 65% |
Tb | 5% | 1% | 20% | Ti | 40% | 23% | 47% |
Dy | 0% | 0% | 0% | Pb | 42% | 79% | 78% |
Ho | 5% | 0% | 14% | Th | 13% | 2% | 15% |
Er | 0% | 0% | 2% | U | 13% | 2% | 15% |
表3 不同母岩类型锆石微量元素数据量缺失情况
Table 3 Missing data of zircon trace elements from different provenance
元素 | 元素数据缺失率 | 元素 | 元素数据缺失率 | ||||
---|---|---|---|---|---|---|---|
热液 锆石 | 岩浆 锆石 | 变质 锆石 | 热液 锆石 | 岩浆 锆石 | 变质 锆石 | ||
La | 0% | 7% | 23% | Tm | 4% | 1% | 17% |
Ce | 0% | 0% | 0% | Yb | 0% | 0% | 0% |
Pr | 0% | 1% | 4% | Lu | 0% | 0% | 12% |
Nd | 0% | 0% | 2% | Y | 20% | 7% | 38% |
Sm | 0% | 0% | 1% | Hf | 27% | 8% | 41% |
Eu | 1% | 0% | 1% | Nb | 38% | 23% | 58% |
Gd | 0% | 0% | 0% | Ta | 36% | 67% | 65% |
Tb | 5% | 1% | 20% | Ti | 40% | 23% | 47% |
Dy | 0% | 0% | 0% | Pb | 42% | 79% | 78% |
Ho | 5% | 0% | 14% | Th | 13% | 2% | 15% |
Er | 0% | 0% | 2% | U | 13% | 2% | 15% |
模型 | 准确率 | 最优超参数 |
---|---|---|
SVM(linear) | 0.868 | C = 16 |
SVM(RBF) | 0.802 | C=16,gamma = 0.5 |
KNN | 0.860 | weights = distance, n neighbors = 1 |
ANN | 0.899 | alpha = 0.001, activation = tanh, hidden layer sizes = (20,1) |
Random Forest | 0.878 | n estimators = 100, max depth = 30, max features = 16 |
表4 4种模型的最优超参数和在测试集的准确率
Table 4 Hyper-parameters of the 4 models and the accuracy on the test set
模型 | 准确率 | 最优超参数 |
---|---|---|
SVM(linear) | 0.868 | C = 16 |
SVM(RBF) | 0.802 | C=16,gamma = 0.5 |
KNN | 0.860 | weights = distance, n neighbors = 1 |
ANN | 0.899 | alpha = 0.001, activation = tanh, hidden layer sizes = (20,1) |
Random Forest | 0.878 | n estimators = 100, max depth = 30, max features = 16 |
锆石类型 | 精确率 | 召回率 | F1分数 | 数量 |
---|---|---|---|---|
热液锆石 | 0.855 | 0.855 | 0.855 | 76 |
岩浆锆石 | 0.857 | 0.868 | 0.863 | 76 |
变质锆石 | 0.893 | 0.882 | 0.887 | 76 |
宏平均(macro) | 0.868 | 228 |
表5 测试集在支持向量机模型中的精确率、召回率、F1分数
Table 5 Accuracy, recall, and F1 score of the test set in the SVM
锆石类型 | 精确率 | 召回率 | F1分数 | 数量 |
---|---|---|---|---|
热液锆石 | 0.855 | 0.855 | 0.855 | 76 |
岩浆锆石 | 0.857 | 0.868 | 0.863 | 76 |
变质锆石 | 0.893 | 0.882 | 0.887 | 76 |
宏平均(macro) | 0.868 | 228 |
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