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arXiv · 2110.13748

SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data

Juan Castorena·Diane Oyen
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Abstract

In this work we propose a deep learning approach to clean spectroscopy signals using only uncleaned data. Cleaning signals from spectroscopy instrument noise is challenging as noise exhibits an unknown, non-zero mean, multivariate distributions. Our framework is a siamese neural net that learns identifiable disentanglement of the signal and noise components under a stationarity assumption. The disentangled representations satisfy reconstruction fidelity, reduce consistencies with measurements of unrelated targets and imposes relaxed-orthogonality constraints between the signal and noise representations. Evaluations on a laser induced breakdown spectroscopy (LIBS) dataset from the ChemCam instrument onboard the Martian Curiosity rover show a superior performance in cleaning LIBS measurements compared to the standard feature engineered approaches being used by the ChemCam team.

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BibTeXRIS

Juan Castorena, Diane Oyen. 2021-10-26. SpectroscopyNet: Learning to pre-process Spectroscopy Signals without clean data. https://arxiv.org/abs/2110.13748

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