SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder
SIGMA: Spectral Interpretation Using Gaussian Mixtures and Autoencoder
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DOI：
10.1029/2022gc010530
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发表时间：
2023-01
期刊：
影响因子：
3.7
通讯作者：
Po‐Yen Tung;H. A. Sheikh;M. Ball;F. Nabiei;R. Harrison
中科院分区：
文献类型：
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作者：
Po‐Yen Tung;H. A. Sheikh;M. Ball;F. Nabiei;R. Harrison
未知微米和纳米级矿物相的识别通常通过分析从高光谱成像数据集生成的化学图来实现，特别是扫描电子显微镜-能量色散X射线光谱（SEM-EDS）。然而，矿物鉴定的准确性和可靠性通常受到主观人为解释、非理想样品制备以及电子束相互作用体积内产生的混合化学信号的存在的限制。机器学习已经成为克服这些问题的强大工具。在这里，我们提出了一种机器学习方法来识别未知相并将其重叠的化学信号解混。该方法利用高斯混合建模聚类的指导，该高斯混合建模聚类拟合在使用神经网络自动编码器建模的逐像素元素数据点的信息潜在空间上，并且使用非负矩阵分解来解混相的重叠化学信号。我们使用两个SEM-EDS数据集评估了新方法的可靠性和准确性：合成混合物样本和真实的颗粒物样本。在前者中，所提出的方法成功地识别了所有主要相，并提取了扣除背景的单相化学信号。未混合的化学光谱显示与地面真实光谱的平均相似性为83.0%。在第二种情况下，该方法证明了识别潜在磁性含铁颗粒及其背景扣除化学信号的能力。我们展示了一种灵活且适应性强的方法，可以以全自动化的方式显着改进矿物学和化学分析。建议的分析过程已内置到用户友好的Python代码中，并具有图形用户界面，便于一般用户使用。
Identification of unknown micro‐ and nano‐sized mineral phases is commonly achieved by analyzing chemical maps generated from hyperspectral imaging data sets, particularly scanning electron microscope—energy dispersive X‐ray spectroscopy (SEM‐EDS). However, the accuracy and reliability of mineral identification are often limited by subjective human interpretation, non‐ideal sample preparation, and the presence of mixed chemical signals generated within the electron‐beam interaction volume. Machine learning has emerged as a powerful tool to overcome these problems. Here, we propose a machine‐learning approach to identify unknown phases and unmix their overlapped chemical signals. This approach leverages the guidance of Gaussian mixture modeling clustering fitted on an informative latent space of pixel‐wise elemental data points modeled using a neural network autoencoder, and unmixes the overlapped chemical signals of phases using non‐negative matrix factorization. We evaluate the reliability and the accuracy of the new approach using two SEM‐EDS data sets: a synthetic mixture sample and a real‐world particulate matter sample. In the former, the proposed approach successfully identifies all major phases and extracts background‐subtracted single‐phase chemical signals. The unmixed chemical spectra show an average similarity of 83.0% with the ground truth spectra. In the second case, the approach demonstrates the ability to identify potentially magnetic Fe‐bearing particles and their background‐subtracted chemical signals. We demonstrate a flexible and adaptable approach that brings a significant improvement to mineralogical and chemical analysis in a fully automated manner. The proposed analysis process has been built into a user‐friendly Python code with a graphical user interface for ease of use by general users.