Robust Spectral Anomaly Detection in EELS Spectral Images via 3D Convolutional Variational Autoencoders

A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and...

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Vydané v:	Small (Weinheim an der Bergstrasse, Germany) Ročník 21; číslo 33; s. e2503019 - n/a
Hlavní autori:	Sultanov, Seyfal, Ayyubi, R. A. W., Buban, James P., Klie, Robert F.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Germany Wiley Subscription Services, Inc 01.08.2025 John Wiley and Sons Inc
Predmet:	Anomalies anomaly detection Automation convolutional variational autoencoders Defects Electron energy electron energy loss spectroscopy Image reconstruction Performance evaluation Principal components analysis Robustness Spectra spectral anomalies spectral imaging Spectrum analysis convolutional variational autoencoders spectral imaging anomaly detection spectral anomalies electron energy loss spectroscopy
ISSN:	1613-6810, 1613-6829, 1613-6829
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Shrnutí:	A 3D Convolutional Variational Autoencoder (3D‐CVAE) is introduced for automated anomaly detection in electron energy‐loss spectroscopy spectrum imaging (EELS‐SI) data. This approach leverages the full 3D structure of EELS‐SI data to detect subtle spectral anomalies while preserving both spatial and spectral correlations across the datacube. By employing cross‐entropy loss and training on bulk spectra, the model learns to reconstruct bulk features characteristic of the defect‐free material. In exploring methods for anomaly detection, both the 3D‐CVAE approach and principal component analysis (PCA) are evaluated, testing their performance using Fe L‐edge ΔE peak shifts designed to simulate material defects. These results show that 3D‐CVAE achieves superior anomaly detection and maintains consistent performance across various shift magnitudes. The method demonstrates clear bimodal separation between bulk and anomalous spectra, enabling reliable classification. Further analysis verifies that lower‐dimensional representations are robust to anomalies in the data. While performance advantages over PCA diminish with decreasing anomaly concentration, our method maintains high reconstruction quality even in challenging, noise‐dominated spectral regions. This approach provides a robust framework for unsupervised automated detection of spectral anomalies in EELS‐SI data, particularly valuable for analyzing complex material systems. Automated anomaly detection is demonstrated for electron energy‐loss spectrum imaging in an atomic‐resolution scanning transmission electron microscope using an unsupervised learning approach. A 3D convolutional variational autoencoder is introduced and tested on the iron L‐edge spectra taken from a single‐crystal BiFeO3 sample. This approach is benchmarked against Principal Component Analysis and high reconstruction quality is demonstrated even in challenging, noise‐dominated spectral regions.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1613-6810 1613-6829 1613-6829
DOI:	10.1002/smll.202503019