Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration

Version 1 : Received: 29 October 2021 / Approved: 3 November 2021 / Online: 3 November 2021 (09:24:38 CET)

How to cite: Kissi, J.; Xie, T.; McIsaac, K.; Osinski, G.R.; Shieh, S. GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration. Preprints 2021, 2021110078 (doi: 10.20944/preprints202111.0078.v1). Kissi, J.; Xie, T.; McIsaac, K.; Osinski, G.R.; Shieh, S. GRaVN: A Convolutional Network Approach to Generalised Characterisation of Raman Spectra for Space Exploration. Preprints 2021, 2021110078 (doi: 10.20944/preprints202111.0078.v1).

Abstract

During planetary exploration mission operations, one of the key responsibilities of the instrument teams to determine data viability for subsequent analysis. During the 2019 CanMoon Lunar Sample Return Analogue Mission, the Lead Raman Specialist manually examined each spectra to provide quality assurance/validation. This non-trivial process requires years of experience to complete accurately. With the proven efficacy of Convolutional Neural Networks (CNNs) in classification tasks, and the increased use of automation and control loops on planetary space platforms for navigation and science targeting, an opportunity presents itself to approach this validation problem utilising CNNs. We present the Generalised Raman Validation Network (GRaVN), an neural network focused specifically on extracting the generalised structure of Raman spectra for quality assurance/validation. This work demonstrates the viability of utilising a CNN network in validation activities for Raman spectroscopy. Utilising only two hidden layers, a configuration was developed that provided good levels of accuracy on a manually curated dataset. This indicates that such a system could be useful as part of an autonomous control loop during planetary exploration activities.

Keywords

GRaVN; machine learning; convolutional neural networks; CNN; raman spectroscopy; analogue missions; planetary science; random undersampling; random oversampling; CanMoon

Subject

ENGINEERING, Electrical & Electronic Engineering

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