用拉曼光谱预测矿物产地，准确率达93%
From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data
- 用一维卷积神经网络分析光谱数据
- 跨101国3.2万样本，平均准确率93%
- 适合地质与材料科学中的产地溯源
准确确定矿物样本的地理来源对地质学、矿物学和材料科学至关重要。本研究利用RRUFF数据库中全面的拉曼光谱数据，提出一种新型机器学习框架，旨在实现矿物标本在国家层面的地理定位。我们采用一维ConvNeXt1D神经网络架构，仅基于光谱特征对矿物光谱进行分类。处理的数据集包含超过32,900个矿物样本，主要为天然样本，覆盖101个国家。通过五折交叉验证，ConvNeXt1D模型实现了93%的平均分类准确率，证明其能有效捕捉拉曼光谱中蕴含的地理空间模式。
原文摘要 · Abstract (English)
Accurately determining the geographic origin of mineral samples is pivotal for applications in geology, mineralogy, and material science. Leveraging the comprehensive Raman spectral data from the RRUFF database, this study introduces a novel machine learning framework aimed at geolocating mineral specimens at the country level. We employ a one-dimensional ConvNeXt1D neural network architecture to classify mineral spectra based solely on their spectral signatures. The processed dataset comprises over 32,900 mineral samples, predominantly natural, spanning 101 countries. Through five-fold cross-validation, the ConvNeXt1D model achieved an impressive average classification accuracy of 93%, demonstrating its efficacy in capturing geospatial patterns inherent in Raman spectra.
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