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A Probabilistic Approach to Remote Compositional Analysis of Planetary Surfaces

Lapotre, M. G. A. and Ehlmann, B. L. and Minson, S. E. (2017) A Probabilistic Approach to Remote Compositional Analysis of Planetary Surfaces. Journal of Geophysical Research: Planets, 122 (5). pp. 983-1009. ISSN 2169-9100. https://resolver.caltech.edu/CaltechAUTHORS:20170302-115016869

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Abstract

Reflected light from planetary surfaces provides information, including mineral/ice compositions and grain sizes, by study of albedo and absorption features as a function of wavelength. However, deconvolving the compositional signal in spectra is complicated by the nonuniqueness of the inverse problem. Trade-offs between mineral abundances and grain sizes in setting reflectance, instrument noise, and systematic errors in the forward model are potential sources of uncertainty, which are often unquantified. Here we adopt a Bayesian implementation of the Hapke model to determine sets of acceptable-fit mineral assemblages, as opposed to single best fit solutions. We quantify errors and uncertainties in mineral abundances and grain sizes that arise from instrument noise, compositional end members, optical constants, and systematic forward model errors for two suites of ternary mixtures (olivine-enstatite-anorthite and olivine-nontronite-basaltic glass) in a series of six experiments in the visible-shortwave infrared (VSWIR) wavelength range. We show that grain sizes are generally poorly constrained from VSWIR spectroscopy. Abundance and grain size trade-offs lead to typical abundance errors of ≤1 wt % (occasionally up to ~5 wt %), while ~3% noise in the data increases errors by up to ~2 wt %. Systematic errors further increase inaccuracies by a factor of 4. Finally, phases with low spectral contrast or inaccurate optical constants can further increase errors. Overall, typical errors in abundance are <10%, but sometimes significantly increase for specific mixtures, prone to abundance/grain-size trade-offs that lead to high unmixing uncertainties. These results highlight the need for probabilistic approaches to remote determination of planetary surface composition.


Item Type:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1002/2016JE005248DOIArticle
https://epsp.stanford.edu/downloadsRelated ItemCode associated with paper
ORCID:
AuthorORCID
Lapotre, M. G. A.0000-0001-9941-1552
Ehlmann, B. L.0000-0002-2745-3240
Minson, S. E.0000-0001-5869-3477
Contact Email Address:lapotremathieu@hotmail.com
Additional Information:© 2017 The Authors. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made. Received 20 DEC 2016; Accepted 5 APR 2017; Accepted article online 14 APR 2017; Published online 26 MAY 2017. We thank Ryan Anderson and Tim Titus of the U.S. Geological Survey for their comments, and Yang Liu and William Farrand for their constructive reviews of our manuscript. We also thank James Beck for early discussions on MCMC methods. This research utilizes spectra acquired by Carle Pieters, John Mustard, and Bethany Ehlmann with the NASA/Keck RELAB facility at Brown University. All MATLAB scripts to run the MCMC procedure are available for download at http://resolver.caltech.edu/CaltechAUTHORS:20170302-115016869. M.G.A.L. was partially funded by a NASA Earth and Space Science Fellowship (12-PLANET12F-0071) and from a MSL Participating Scientist Program grant to B.L.E. We acknowledge the support of a NASA PGG grant to John Mustard for acquisition of mixture data for experiments 5 and 6. Laboratory spectra presented in this paper are available in the RELAB Brown/NASA-Keck spectral library.
Group:Astronomy Department
Funders:
Funding AgencyGrant Number
NASA12-PLANET12F-0071
Mars Science Laboratory (MSL)UNSPECIFIED
Subject Keywords:spectroscopy; MCMC; planetary surfaces; VSWIR; Hapke
Issue or Number:5
Record Number:CaltechAUTHORS:20170302-115016869
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20170302-115016869
Official Citation:Lapotre, M. G. A., B. L. Ehlmann, and S. E. Minson (2017), A probabilistic approach to remote compositional analysis of planetary surfaces, J. Geophys. Res. Planets, 122, 983–1009, doi:10.1002/2016JE005248
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:74666
Collection:CaltechAUTHORS
Deposited By: Gail Peretsman Clement
Deposited On:06 Mar 2017 20:53
Last Modified:13 Jan 2021 16:48

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