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.

中文翻译：

---

锆石分类：一种使用锆石地球化学的机器学习方法


本研究通过对包含 27,818 组锆石微量元素集的数据集进行全面分析，提出了一种新颖、快速和准确的方法来确定锆石来源。此方法将反向传播神经网络与 AdaBoost 算法集成在一起。最佳分类器的特点是两层神经网络模型的线性组合，包括 100 个基本分类器和 400 个隐藏神经元。它经过了 1000 多次迭代的严格训练，结果是 8.31% 的无偏错误率。为了便于实际应用，该分类器被集成到一个支持宏的 Excel 电子表格中。