T246 Mineral Informatics and the Evolution of Earth, Planets, and Life: In Honor of MSA Awardee, Shaunna M. Morrison (Posters)

Wednesday, 18 October 2023
8:00 AM - 5:30 PM
Hall B

MACHINE LEARNING IN PREDICTING MULTI-COMPONENT MINERAL COMPOSITIONS FOR SPACECRAFT X-RAY DIFFRACTION INSTRUMENTS

Paper number: T246-3
Program: Technical Sessions
Time: 8:00 AM – 5:30 PM

Authors

Abstract

To gain a deeper understanding of the mineral forming environments, geologic history, and potential of past or present habitability, the NASA Mars Science Laboratory CheMin X-ray diffractometer team and collaborators developed crystal-chemical methods for predicting limited chemical compositions of the minerals found in the CheMin samples [1,2]. In this study, we extend the methodology by leveraging a machine learning technique known as Label Distribution Learning (LDL) [3] to forecast multicomponent chemical compositions of mineral phases within Gale Crater and beyond. This application of LDL allows for a more detailed petrologic interpretation of the martian surface's geologic evolution, shedding light on the processes shaping the planet's history.

LDL is an innovative framework designed for classification problems with small datasets, widely recognized for its success in facial recognition tasks such as age estimation. In this research, we adapt the LDL algorithm to predict chemical elements (labels) and their corresponding abundances (degrees) for each martian mineral sample, utilizing crystallographic parameters. The model's performance is assessed based on the distance and similarity between label distributions, as well as mean square error, while also comparing results against traditional machine learning methods.

The adaptation of LDL for mineralogical predictions opens avenues for similar applications in future Martian missions and other planetary exploration endeavors. The capability to infer multicomponent mineral compositions from crystallographic data presents exciting opportunities for advancing our understanding of planetary geology.

[1] Morrison et al. (2018) Am Min, 103(6): 848-856

[2] Morrison et al. (2018) Am Min, 103(6): 857-871

[3] Geng (2016) IEEE Transactions on Knowledge and Data Engineering, 28(7), 1734-1748

[4] Morrison et al. (2018) Predicting Multi-Component Mineral Compositions in Gale crater, Mars with Label Distribution Learning, AGU, P21I-3438

[5] Morrison et al. (2019) Machine Learning in Predicting Multi-Component Mineral Compositions in Gale Crater, Mars, Goldschmidt, Abstract

Geological Society of America Abstracts with Programs. Vol. 55, No. 6

doi: 10.1130/abs/2023AM-395912

© Copyright 2023 The Geological Society of America (GSA), all rights reserved. Permission is hereby granted to the author(s) of this abstract to reproduce and distribute it freely, for noncommercial purposes. Permission is hereby granted to any individual scientist to download a single copy of this electronic file and reproduce up to 20 paper copies for noncommercial purposes advancing science and education, including classroom use, providing all reproductions include the complete content shown here, including the author information. All other forms of reproduction and/or transmittal are prohibited without written permission from GSA Copyright Permissions.