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35-3 Deep learning-assisted Raman spectroscopy for automated identification of specific minerals
Session: Minerals in Motion: Tracking Mineral Reactions Using In Situ and Synchrotron Techniques, A Celebration of the Career of Peter Heaney (Posters)
Poster Booth No.: 332
Presenting Author:
wangtong dong
Author:
dong, wangtong1
(1) zhejiang university, hangzhou, China,
Abstract:
Raman spectroscopy is applied as an important method for material identification in field geology. However, analyzing the collected Raman spectroscopy results is time-consuming and labor-intensive, which arises a demand for labeling and sorting a large volume of in-situ Raman measurements automatically. In this study, we consider the spectral characteristics of mineral to develop a convolutional attention network for rapid and precise identification of mineral component. Moreover, we introduce Gradient-weight Class Activation Mapping Plus Plus(Grad-Cam++) to visualize the important region for predicting. Compared to standard Convolutional Neural Networks (CNN), our model is better at learning the details in characteristic peaks to distinguish minerals with similar Raman spectra. Overall, this study exhibits significance for automated process of labeling data collected by Raman instruments in field work and developing similar spectral recognition algorithms.
Geological Society of America Abstracts with Program. Vol. 57, No. 6, 2025