Convolutional Neural Networks as a Tool for Raman Spectral Mineral Classification Under Low Signal, Dusty Mars Conditions

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AbstractNASA's Mars 2020 and ESA's ExoMars will collect Raman measurements in dusty field conditions obscuring underlying rocks. This presents a challenge for remote Raman measurements at distances where mechanical or ablative sample cleaning is not straightforward. Historically, probing broad lithostratigraphic suites has been thwarted by the need for pristine targets and high‐quality spectra. We provide a means of identifying Raman spectra of common rock‐forming silicate, carbonate, and sulfate minerals under low signal‐to‐noise‐ratios, Mars‐like conditions using a convolutional neural network (CNN). The CNN was trained on the Machine Learning Raman Open Data set data set with 500,000+ Raman spectra of hand samples/powder mixtures (5,000+ spectra/mineral class). Diversity in sample microtopography, orientation, and crystallinity simulated varying laser focuses and spectral quality, and no traditional spectral preprocessing such as cosmic ray or baseline removal was employed. The CNN identified low‐intensity Raman scatterers (micas and amphiboles), mixed minerals, and distinguished between mineral endmembers with +99% success. We present among the first known implementations of “big data” machine learning using varied, high‐volume Raman spectral datasets. The pattern recognition abilities of CNNs can facilitate scientist Raman spectral interpretation on Earth and autonomous rover decision‐making on planets like Mars; increasing scientific yield, correcting human classification errors, reducing the need for thorough target dust removal during evaluative measurements, and streamlining the data communications pipeline—saving time and resources. This study examines an end‐to‐end development process for creating a deep learning algorithm sensitive to varieties of Raman spectra and provides guidelines for CNN model development at the interface of Raman spectroscopy, deep learning, and planetary science.