GSA Connects 2021 in Portland, Oregon

Paper No. 146-8
Presentation Time: 10:10 AM


MUSTARD, John1, PARENTE, Mario2, DAS, Eashan1, LIN, Honglei3 and WU, Wing1, (1)Earth, Environmental and Planetary Scienes, Brown University, 324 Brook street, Box 1846, Brown University, Providence, RI 02912, (2)Electrical and Computer Engineering, University of Massachusetts, Amherst, MA 01003, (3)Institute of Geology and Geophysics, Chinese Academy of Sciences (IGGCAS), No. 19, Beitucheng west Road, Beijing, China

The power of reflectance and emittance spectroscopy across visible to mid-infrared wavelengths is to detect minerals and quantify their abundance on planetary surfaces. When data were acquired mostly with telescopes this was a process of matching spectral features observed remotely with observations made of known samples in laboratories. However the explosion in data volume from imaging spectrometers such as OMEGA, CRISM, Moon Mineralogy Mapper and VIRTIS as well as point spectrometers such as ChemCam and OTES presents significant challenges to sifting through all these data across multiple planetary targets. Advances in data sciences have delivered novel and promising approaches to searching for the proverbial needles in the haystacks of data. Using imaging spectrometer data of laboratory mixtures for which we know the mixture components including particle sizes we have been validating mineral detection and abundance algorithms. There are huge differences in the quality of data collected in the laboratory on carefully prepared samples to those collected remotely of natural surfaces related to the viewing geometry, surface textures and presence of obscuring factors such as dust alteration rinds. In additional instruments have noise and other properties very different from the lab, not to mention interference from an atmosphere if present. We have thoroughly evaluated the mineral detection technique Factor Analysis/Target Transformation using laboratory imaging spectrometer data of mixtures of 9 target minerals like gypsum, montmorillonite and calcite with the Mars Global Simulatant-1. The conclude the approach can reliably detect the minerals used when present in abundances >10%. The approach was modified to obtain the spatial distribution of target minerals in a hyperspectral image and successfully applied to detect hydrated silica in the Jezero Crater delta with CRISM data, potential targets for the Perseverance Rover. A new serpentine outcrop was also detected using this approach with CRISM data in Nili Fossae. The approach now being applied to Chemcam passive point reflectance spectra along the Curiosity traverse show promise for novel mineral detections but will require further refined analyses as well as laboratory validation of the point spectroscopy approach.