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

The SOS-framework (Systems of Sedentary behaviours): an international transdisciplinary consensus framework for the study of determinants, research priorities and policy on sedentary behaviour across the life course: a DEDIPAC-study.

BACKGROUND: Ecological models are currently the most used approaches to classify and conceptualise determinants of sedentary behaviour, but these approaches are limited in their ability to capture the complexity of and interplay between determinants. The aim of the project described here was to develop a transdisciplinary dynamic framework, grounded in a system-based approach, for research on determinants of sedentary behaviour across the life span and intervention and policy planning and evaluation. METHODS: A comprehensive concept mapping approach was used to develop the Systems Of Sedentary behaviours (SOS) framework, involving four main phases: (1) preparation, (2) generation of statements, (3) structuring (sorting and ranking), and (4) analysis and interpretation. The first two phases were undertaken between December 2013 and February 2015 by the DEDIPAC KH team (DEterminants of DIet and Physical Activity Knowledge Hub). The last two phases were completed during a two-day consensus meeting in June 2015. RESULTS: During the first phase, 550 factors regarding sedentary behaviour were listed across three age groups (i.e., youths, adults and older adults), which were reduced to a final list of 190 life course factors in phase 2 used during the consensus meeting. In total, 69 international delegates, seven invited experts and one concept mapping consultant attended the consensus meeting. The final framework obtained during that meeting consisted of six clusters of determinants: Physical Health and Wellbeing (71% consensus), Social and Cultural Context (59% consensus), Built and Natural Environment (65% consensus), Psychology and Behaviour (80% consensus), Politics and Economics (78% consensus), and Institutional and Home Settings (78% consensus). Conducting studies on Institutional Settings was ranked as the first research priority. The view that this framework captures a system-based map of determinants of sedentary behaviour was expressed by 89% of the participants. CONCLUSION: Through an international transdisciplinary consensus process, the SOS framework was developed for the determinants of sedentary behaviour through the life course. Investigating the influence of Institutional and Home Settings was deemed to be the most important area of research to focus on at present and potentially the most modifiable. The SOS framework can be used as an important tool to prioritise future research and to develop policies to reduce sedentary time

Investigating the Evolution of Surface Water on Mars through Spectroscopy of Secondary Minerals

Despite its current arid climate, Mars’ surface preserves a wide variety of morphologies and minerals that point to a water-rich past. However, the mechanisms and timing of this environmental change are not yet well understood. In this dissertation, we explore a variety of water-related minerals through visible-shortwave infrared (VSWIR) reflectance spectroscopy to better understand the environmental conditions at the time of their formation, and trace the evolution of surface water on Mars over time. We also demonstrate the capabilities of VSWIR spectroscopy at laboratory and field scales in a Mars analogue environment (Samail Ophiolite, Oman)—an emerging technique for use on future landed missions that enables us to differentiate between spectrally-similar minerals and spot rare minerals that help to constrain environmental conditions and better understand the geologic context of samples. On Mars, we use orbital datasets (predominantly CRISM, the Compact Reconnaissance Imaging Spectrometer for Mars) to investigate secondary minerals in the southern highlands of Mars, focusing on perchlorate, chloride, and sulphate minerals. We identify a previously unknown artifact in the CRISM dataset, which mimics perchlorate absorptions; previous orbital perchlorate detections (including those associated with recurring slope lineae) are not robust when data are reprocessed, suggesting that there may not be orbitally-detectable reservoirs of perchlorate on Mars, which would enable liquid brines to exist at the surface today. A detailed investigation of chloride deposits across the southern highlands of Mars points to an episodic surface-runoff water source rather than upwelling groundwater, a process which continued to create chloride deposits into the Amazonian era. Where chloride and sulphate deposits are in close proximity (Terra Sirenum, Mars), they do not appear to be genetically related as they often are on Earth; instead, they point to chemically distinct groundwater vs. surface water reservoirs in Terra Sirenum through the Hesperian and into the Amazonian. Together, these studies indicate that briny and/or acidic volumes of water at the surface capable of creating mineral deposits continued to exist — at least episodically — on Mars into the Amazonian, rather than ceasing much earlier in Mars’ history.</p

Distinct Alteration Environments in Terra Sirenum, Mars

Data returned from a variety of satellite missions to Mars have shown that the Terra Sirenum region of the southern highlands is unique area. Here, a diverse array of secondary minerals are found in close proximity, including chlorides, aluminum and iron/magnesium phyllosilicates, acidic sulphates (alunite and jarosite), as well as several calcium/magnesium sulphates. These minerals require the presence of water to form, so can help us understand the history of water in Terra Sirenum

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