Classifying zircon: A machine-learning approach using zircon geochemistry

IF 7.2 1区地球科学 Q1 GEOSCIENCES, MULTIDISCIPLINARY

Gondwana Research Pub Date : 2024-10-03 DOI:10.1016/j.gr.2024.09.010

Jintao Kong , Hongru Yu , Junyi Sun , Huan Zhang , Miaomiao Zhang , Zhi Xia

引用次数: 0

Abstract

This study presented a novel, rapid, and accurate method for determining zircon origin via a comprehensive analysis of a dataset containing 27,818 zircon trace element sets. This method integrated back propagation neural networks with the AdaBoost algorithm. The optimal classifier characterized as a linear combination of a two-layer neural network model, comprised 100 base classifiers and 400 hidden neurons. It was rigorously trained over 1000 iterations, which resulted in an unbiased error rate of 8.31%. To facilitate practical application, the classifier was integrated into a macro-enabled Excel spreadsheet.

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锆石分类：利用锆石地球化学的机器学习方法

本研究通过对包含 27,818 个锆石痕量元素集的数据集进行综合分析，提出了一种新颖、快速、准确的方法来确定锆石的来源。该方法将反向传播神经网络与 AdaBoost 算法相结合。最佳分类器的特征是双层神经网络模型的线性组合，由 100 个基本分类器和 400 个隐藏神经元组成。经过 1000 次迭代的严格训练，其无偏误差率为 8.31%。为便于实际应用，该分类器被集成到一个支持宏的 Excel 电子表格中。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Gondwana Research 地学-地球科学综合

CiteScore

12.90

自引率

6.60%

发文量

298

审稿时长

65 days

期刊介绍： Gondwana Research (GR) is an International Journal aimed to promote high quality research publications on all topics related to solid Earth, particularly with reference to the origin and evolution of continents, continental assemblies and their resources. GR is an "all earth science" journal with no restrictions on geological time, terrane or theme and covers a wide spectrum of topics in geosciences such as geology, geomorphology, palaeontology, structure, petrology, geochemistry, stable isotopes, geochronology, economic geology, exploration geology, engineering geology, geophysics, and environmental geology among other themes, and provides an appropriate forum to integrate studies from different disciplines and different terrains. In addition to regular articles and thematic issues, the journal invites high profile state-of-the-art reviews on thrust area topics for its column, ''GR FOCUS''. Focus articles include short biographies and photographs of the authors. Short articles (within ten printed pages) for rapid publication reporting important discoveries or innovative models of global interest will be considered under the category ''GR LETTERS''.