Deep Learning Investigation of Mercury’s Explosive Volcanism

Abstract

The remnants of explosive volcanism on Mercury have been observed in the form of vents and pyroclastic deposits, termed faculae, using data from the Mercury Atmospheric and Surface Composition Spectrometer (MASCS) onboard the Mercury surface, space environment, geochemistry, and ranging (MESSENGER) spacecraft. Although these features present a wide variety of sizes, shapes, and spectral properties, the large number of observations and the lack of high-resolution hyperspectral images complicates their detailed characterisation. We investigate the application of unsupervised deep learning to explore the diversity and constrain the extent of the Hermean pyroclastic deposits. We use a three-dimensional convolutional autoencoder (3DCAE) to extract the spectral and spatial attributes that characterise these features and to create cluster maps constructing a unique framework to compare different deposits. From the cluster maps we define the boundaries of 55 irregular deposits covering 110 vents and compare the results with previous radius and surface estimates. We find that the network is capable of extracting spatial information such as the border of the faculae, and spectral information to altogether highlight the pyroclastic deposits from the background terrain. Overall, we find the 3DCAE an effective technique to analyse sparse observations in planetary sciences.