Gaining a global perspective on the surface composition of Venus from
orbit through near infrared observations -- with a little help from
machine learning approaches
Abstract
Venus is the most Earth-like of the terrestrial planets, though very
little is known about its surface composition. Thanks to recent advances
in laboratory spectroscopy and spectral analysis techniques, this is
about to change. Although the atmosphere prohibits observations of the
surface with traditional imaging techniques over much of the EM spectral
range, five transparent windows between ~0.86 µm and
~1.18 µm occur in the atmosphere’s CO2 spectrum. New
high temperature laboratory spectra from the Planetary Spectroscopy
Laboratory at DLR show that spectra in these windows are highly
diagnostic for surface mineralogy [1]. The Venus Emissivity Mapper
(VEM) [2] builds on these recent advances VEM is the first flight
instrument specially designed to focus solely on mapping Venus’ surface
using the windows around 1 µm. Operating in situ from Venus orbit, VEM
will provide a global map of composition as well as redox state of the
surface, enabling a comprehensive picture of surface-atmosphere
interaction on Venus. VEM will return a complex data set containing
surface, atmospheric, cloud, and scattering information. Total planned
data volume for a typical mission scenario exceeds 1TB. Classical
analysis techniques have been successfully used for VIRTIS on Venus
Express [3-5] and could be employed with the VEM data. However,
application of machine learning approaches to this rich dataset is
vastly more efficient, as has already been confirmed with laboratory
data. Binary classifiers [6] demonstrate that at current best
estimate errors, basalt spectra are confidently discriminated from
basaltic andesites, andesites, and rhyolite/granite. Applying the
approach of self-organizing maps to the increasingly large set of
laboratory measurements allows searching for additional mineralogical
indicators, especially including their temperature dependence. [1]
Dyar M. D. et al. 2017 LPS XLVIII, #1512. [2] Helbert, J. et al.
2016. San Diego, CA, SPIE. [3] Smrekar, S.E., et al. Science, 2010
328(5978), 605-8. [4] Helbert, J., et al., GRL, 2008 35(11). [5]
Mueller, N., et al., JGR, 2008 113. [6] Dyar M. D. et al. 2017 LPS
XLVIII, #3014.