Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) imagery. Its utility in detecting rare phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date Jezero delta or groundwater alteration of portions of the Jezero sedimentary sequence

Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero Crater and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) images. Its utility in detecting small exposures of uncommon phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date the Jezero crater delta or groundwater alteration of portions of the Jezero crater sedimentary sequence

Machine-Learning-Driven New Geologic Discoveries at Mars Rover Landing Sites: Jezero Crater and NE Syrtis

A hierarchical Bayesian classifier is trained at pixel scale with spectral data from the CRISM (Compact Reconnaissance Imaging Spectrometer for Mars) images. Its utility in detecting small exposures of uncommon phases is demonstrated with new geologic discoveries near the Mars-2020 rover landing site. Akaganeite is found in sediments on the Jezero crater floor and in fluvial deposits at NE Syrtis. Jarosite and silica are found on the Jezero crater floor while chlorite-smectite and Al phyllosilicates are found in the Jezero crater walls. These detections point to a multi-stage, multi-chemistry history of water in Jezero crater and the surrounding region and provide new information for guiding the Mars-2020 rover's landed exploration. In particular, the akaganeite, silica, and jarosite in the floor deposits suggest either a later episode of salty, Fe-rich waters that post-date the Jezero crater delta or groundwater alteration of portions of the Jezero crater sedimentary sequence

Identifying and Quantifying Mineral Abundance through VSWIR Microimaging Spectroscopy: A Comparison to XRD and SEM

Visible-shortwave infrared microimaging reflectance spectroscopy is a new technique to identify minerals, quantify abundances, and assess textural relationships at sub-millimetre scale without destructive sample preparation. Here we used a prototype instrument to image serpentinized igneous rocks and carbonate-rich travertine deposits to evaluate performance, relative to traditional techniques: XRD (mineralogical analysis of bulk powders with no texture preservation) and SEM/EDS (analysis of phases and textures using chemical data from polished thin sections). VSWIR microimaging spectroscopy is ideal for identifying spatially coherent rare phases, below XRD detection limits. The progress of alteration can also be inferred from spectral parameters and may correspond to phases that are amorphous in XRD. However, VSWIR microimaging spectroscopy can sometimes be challenging with analyses of very dark materials (reflectance <0.05) and mineral mixtures occurring at a spatial scales multiple factors below the pixel size. Abundances derived from linear unmixing typically agree with those from XRD and EDS within ~10%

A machine learning toolkit for CRISM image analysis

Hyperspectral images collected by remote sensing have played a significant role in the discovery of aqueous alteration minerals, which in turn have important implications for our understanding of the changing habitability on Mars. Traditional spectral analyzes based on summary parameters have been helpful in converting hyperspectral cubes into readily visualizable three channel maps highlighting high-level mineral composition of the Martian terrain. These maps have been used as a starting point in the search for specific mineral phases in images. Although the amount of labor needed to verify the presence of a mineral phase in an image is quite limited for phases that emerge with high abundance, manual processing becomes laborious when the task involves determining the spatial extent of detected phases or identifying small outcrops of secondary phases that appear in only a few pixels within an image. Thanks to extensive use of remote sensing data and rover expeditions, significant domain knowledge has accumulated over the years about mineral composition of several regions of interest on Mars, which allow us to collect reliable labeled data required to train machine learning algorithms. In this study we demonstrate the utility of machine learning in two essential tasks for hyperspectral data analysis: nonlinear noise removal and mineral classification. We develop a simple yet effective hierarchical Bayesian model for estimating distributions of spectral patterns and extensively validate this model for mineral classification on several test images. Our results demonstrate that machine learning can be highly effective in exposing tiny outcrops of specific phases in orbital data that are not uncovered by traditional spectral analysis. We package implemented scripts, documentation illustrating use cases, and pixel-scale training data collected from dozens of well-characterized images into a new toolkit. We hope that this new toolkit will provide advanced and effective processing tools and improve community’s ability to map compositional units in remote sensing data quickly, accurately, and at scale

Data accompanying "Evidence for deposition of chloride on Mars from small-volume surface water events into the Late Hesperian-Early Amazonian"

This data accompanies Leask &amp; Ehlmann, AGU Advances, 2021. We provide two Excel files (.xls) of all of the statistics from examination of the Osterloo et al. 2010 chloride deposits relative to MOLA, CTX, CRISM, and HiRISE. We also provide a comma-separated value file (.csv) that maps the chloride deposits to their geologic unit in the Tanaka et al 2014 global geological map.Related Publication:&lt;/p&gt; Evidence for deposition of chloride on Mars from small-volume surface water events into the Late Hesperian-Early Amazonian&lt;/p&gt; Leask, EK Caltech&lt;/p&gt; Ehlmann, BL Caltech&lt;/p&gt; AGU Advances&lt;/p&gt;en

Identifying and Quantifying Mineral Abundance through VSWIR Microimaging Spectroscopy: A Comparison to XRD and SEM

Visible-shortwave infrared microimaging reflectance spectroscopy is a new technique to identify minerals, quantify abundances, and assess textural relationships at sub-millimetre scale without destructive sample preparation. Here we used a prototype instrument to image serpentinized igneous rocks and carbonate-rich travertine deposits to evaluate performance, relative to traditional techniques: XRD (mineralogical analysis of bulk powders with no texture preservation) and SEM/EDS (analysis of phases and textures using chemical data from polished thin sections). VSWIR microimaging spectroscopy is ideal for identifying spatially coherent rare phases, below XRD detection limits. The progress of alteration can also be inferred from spectral parameters and may correspond to phases that are amorphous in XRD. However, VSWIR microimaging spectroscopy can sometimes be challenging with analyses of very dark materials (reflectance &lt;0.05) and mineral mixtures occurring at a spatial scales multiple factors below the pixel size. Abundances derived from linear unmixing typically agree with those from XRD and EDS within ~10%.Sample: A rock was collected from an travertine conglomerate, containing ophiolite clasts. The rock was collected in Oman near the Samail ophiolite (collected Jan 2012; B. Ehlmann; analyzed in Leask &amp; Ehlmann, 2016). Data included are: 1) UCIS (Ultra-Compact Imaging Spectrometer; B. Van Gorp et al) data cube. Data near ends of sensor may be suspect [e.g. under 500 nm, over 2500 nm]. {OM12L_001_UCIS_cube_masked} 2) EDS-SEM mosaic image cube (4x downsampled to despeckle, using a nearest neighbour algorithm). Acquired Caltech October 2015. Chemical data are in atomic %. {om12L_001_SEM_4x_masked} 3) EDS-SEM data warped to UCIS cube [degree 4 convolution, using built-in ENVI warping algorithm and hand-picked ground-control points]. Three UCIS images were mosaicked together to build the base image. Note whole image difficult to match 100% perfectly; easier to line up a smaller subset. {OM12L_001_warp_SEM_to_UCIS} 4) XRD data (quantitative analysis done Aug 2015 by Actlabs). Note that the subsamples of the rock crushed for XRD analysis are not the same subsample imaged, although all come from the same rock. 5) Photographs of rock sample for context (taken by C.Sanders, 2014). {Macro_OM12L_001.JPG; OM12L_001_smoothface_2.jpg}Related Publication:&lt;/p&gt; Identifying and Quantifying Mineral Abundance through VSWIR Microimaging Spectroscopy: A Comparison to XRD and SEM&lt;/p&gt; Leask, Ellen K. Division of Geological &amp; Planetary Sciences, California Institute of Technology&lt;/p&gt; Ehlmann, Bethany L. Division of Geological &amp; Planetary Sciences, California Institute of Technology&lt;/p&gt; 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016)&lt;/p&gt; 2016-08-24&lt;/p&gt;en

Identifying and Quantifying Mineral Abundance through VSWIR Microimaging Spectroscopy: A Comparison to XRD and SEM

Visible-shortwave infrared microimaging reflectance spectroscopy is a new technique to identify minerals, quantify abundances, and assess textural relationships at sub-millimetre scale without destructive sample preparation. Here we used a prototype instrument to image serpentinized igneous rocks and carbonate-rich travertine deposits to evaluate performance, relative to traditional techniques: XRD (mineralogical analysis of bulk powders with no texture preservation) and SEM/EDS (analysis of phases and textures using chemical data from polished thin sections). VSWIR microimaging spectroscopy is ideal for identifying spatially coherent rare phases, below XRD detection limits. The progress of alteration can also be inferred from spectral parameters and may correspond to phases that are amorphous in XRD. However, VSWIR microimaging spectroscopy can sometimes be challenging with analyses of very dark materials (reflectance &lt;0.05) and mineral mixtures occurring at a spatial scales multiple factors below the pixel size. Abundances derived from linear unmixing typically agree with those from XRD and EDS within ~10%.Sample: A rock was collected from an travertine conglomerate, containing ophiolite clasts. The rock was collected in Oman near the Samail ophiolite (collected Jan 2012; B. Ehlmann; analyzed in Leask &amp; Ehlmann, 2016). Data included are: 1) UCIS (Ultra-Compact Imaging Spectrometer; B. Van Gorp et al) data cube. Data near ends of sensor may be suspect [e.g. under 500 nm, over 2500 nm]. {OM12L_001_UCIS_cube_masked} 2) EDS-SEM mosaic image cube (4x downsampled to despeckle, using a nearest neighbour algorithm). Acquired Caltech October 2015. Chemical data are in atomic %. {om12L_001_SEM_4x_masked} 3) EDS-SEM data warped to UCIS cube [degree 4 convolution, using built-in ENVI warping algorithm and hand-picked ground-control points]. Three UCIS images were mosaicked together to build the base image. Note whole image difficult to match 100% perfectly; easier to line up a smaller subset. {OM12L_001_warp_SEM_to_UCIS} 4) XRD data (quantitative analysis done Aug 2015 by Actlabs). Note that the subsamples of the rock crushed for XRD analysis are not the same subsample imaged, although all come from the same rock. 5) Photographs of rock sample for context (taken by C.Sanders, 2014). {Macro_OM12L_001.JPG; OM12L_001_smoothface_2.jpg}Related Publication: Identifying and Quantifying Mineral Abundance through VSWIR Microimaging Spectroscopy: A Comparison to XRD and SEM Leask, Ellen K. Division of Geological &amp; Planetary Sciences, California Institute of Technology Ehlmann, Bethany L. Division of Geological &amp; Planetary Sciences, California Institute of Technology 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS 2016) 2016-08-24 en

Tracing Carbonate Formation, Serpentinization, and Biological Materials With Micro‐/Meso‐Scale Infrared Imaging Spectroscopy in a Mars Analog System, Samail Ophiolite, Oman

International audienceVisible-shortwave infrared (VSWIR) imaging spectrometers map composition remotely with spatial context, typically at many meters-scale from orbital and airborne data. Here, we evaluate VSWIR imaging spectroscopy capabilities at centimeters to sub-millimeter scale at the Samail Ophiolite, Oman, where mafic and ultramafic lithologies and their alteration products, including serpentine and carbonates, are exposed in a semi-arid environment, analogous to similar mineral associations observed from Mars orbit that will be explored by the Mars-2020 rover. At outcrop and hand specimen scales, VSWIR spectroscopy (a) identifies cross-cutting veins of calcite, dolomite, magnesite, serpentine, and chlorite that record pathways and time-order of multiple alteration events of changing fluid composition; (b) detects small-scale, partially altered remnant pyroxenes and localized epidote and prehnite that indicate protolith composition and temperatures and pressures of multiple generations of faulting and alteration, respectively; and (c) discriminates between spectrally similar carbonate and serpentine phases and carbonate solid solutions. In natural magnesite veins, minor amounts of ferrous iron can appear similar to olivine's strong 1-μm absorption, though no olivine is present. We also find that mineral identification for carbonate and serpentine in mixtures with each other is strongly scale-and texture-dependent; ∼40 area% dolomite in mm-scale veins at one serpentinite outcrop and ∼18 area% serpentine in a calciterich travertine outcrop are not discriminated until spatial scales 1 μm are required to identify most organic materials and distinguish most mineral phases