A Golden Age of Mars Exploration
A Monte Carlo simulation of random residual error in elevation (Z coordinate) was performed to obtain slope, dip, and error estimates of slope and dip measurements for each depositional plane (Table A1). The results of the intimate (non-linear) mixing model are not always better than those calculated with the (linear) checkerboard model.
The Sedimentary Rock Record of Mars
Dissertation Summary
With the advent of high-resolution orbital and rover surveys of Mars, as described above, the study of Martian sedimentary rocks may move from an exploratory phase based primarily on qualitative observations to one in which quantitative analyzes impose limitations on the evolution of depositional and diagenetic environments. on Mars. This study was conducted in the laboratory, but the results are broadly applicable to the detection and quantification of hydrated minerals in sedimentary deposits on Mars.
Data and Methods
- Spacecraft Instrument Data
- Mars Orbiter Laser Altimeter (MOLA)
- Thermal Emission Imaging System (THEMIS)
- High Resolution Imaging Science Experiment (HiRISE)
- Mars Science Laboratory (MSL) Curiosity Rover
- Software
- ArcGIS
- MATLAB
- Laboratory Instruments
- Fourier Transform Infrared Spectrometer (FTIR)
This graphical representation provides an approximation of the probability distribution of bed thickness at each location. With the exception of the NAu-2 (nontronite) blend series (Figure 4.7), the modeled spectra consistently underestimate the strength of the bands associated with the clay minerals.
Introduction
Stratigraphy is most often associated with the study of sedimentary rock records, especially on Earth where 75% of the rocks exposed at the surface above sea level are sedimentary [Tarbuck et al., 2004]. Other studies use available higher-resolution imaging data but are focused on specific regions of Mars, e.g., Quantin et al.
Data and Methods
- Data
- Image Classification
- Spatial Analysis
All images in the database (Figure 2.8a) and all images in the database containing stratified precipitation (Figure 2.8c) were plotted on a THEMIS Day IR equicylindrical projection basemap as single points according to the midlatitude and longitude of each image. The stratified depositional database is also grouped by geomorphic context and site age (Figure 2.9) and setting (basin fill vs. unconfined, Figures and plotted on the geological map to show trends in the distribution, geomorphic setting and depositional type in sites of different ages.
Results
- Global Distribution of Stratified Rocks
- Geomorphic Setting of Stratified Rocks
- Basin Fill Versus Unconfined Stratified Deposits
- Glacial/Periglacial Deposits
Few images containing canyon or channel deposits are found in Noachian-age terranes, which is consistent with the lack of these deposits in the southern highlands of Mars. The plots in Figure 2.12 illustrate the changes in the global distribution and relative proportions of basin fill and unconfined layered deposits in terrain of different ages.
Discussion
- Global Distribution and Comparison to Previous Studies
- Implications for the Martian Sediment and Hydrological Cycles
- Global-Scale Depositional Processes on Mars
The distribution of stratified deposits observed at higher latitudes observed in this study is more consistent with the identification of stratified deposits in high latitude regions also recognized by Schon et al. Periglacial/glacial processes can significantly contribute to the appearance of stratified deposits observed at latitudes above 30°.
Conclusions
Average bed thickness measured at this location using 25 centimeters per pixel orthoimage varies from ~30 centimeters (WJ8) to ~50 centimeters (WJ7), while average bed thickness measured at 1 meter per pixel orthoimages are between ~50 centimeters (WJ3, WJ8) ) to more than 1 meter (WJ7). 80 25 centimeters per pixel and 1 meter per pixel sections, but the minimum and maximum bed thickness measured in the two datasets are similar. This is in accordance with Lilliefor's normality test, which suggests that the normal distribution does not fit all of the 25 centimeters per pixel and 1 meter per pixel measured sections on the plateau west.
The minimum measured layer thickness for all sections, whether measured with a 25 centimeter per pixel orthoimage or a 1 meter per pixel image, is <10 centimeters. However, the maximum bed thickness varies between sections, with the thickest beds measured in the lower parts of the beam. 85 Seven of the eight Gale sections measured with an orthoimage of 25 centimeters per pixel and five of eight sections measured with an orthoimage of 1 meter per pixel reject the null hypothesis of a lognormal logarithm.
However, the majority of sections measured on the plateau west of Juventae are not random according to RUD testing. One explanation for the optimized particle diameter ratios calculated in Table 4.2 and displayed for each blend series in Figure 4.11 is that the ratios represent the actual physical changes in the grain size of the blend components.
Introduction
Background
- Statistical Analysis of Bed Thickness on Earth
- Lognormal Distributions
- Exponential Distributions
- Power-Law Distributions
Bed thickness distribution has also been investigated for peritidal carbonates [Wilkinson et al. Wilkinson and Drummond, 2004; Burgess, 2008], mixed carbonate-clastic deposits [Drummond and Wilkinson, 1996; Wilkinson and Drummond, 2004], debris flows [Rothman and Grotzinger, 1995] and fluvial deposits [Atkinson, 1962]. Scale-invariant power law relationships can also describe the distribution of sedimentary bed thickness [Rothman and Grotzinger, 1995; Awadallah et al., 2001;. Based on the assumption that the bed thickness frequency follows a power law, it is interpreted that there are systematic deviations from the expected power law behavior.
In some cases, bed thickness distributions can even be directly linked to specific depositional environments.
Methods
- Identifying Beds from Orbit on Mars
- Orbital Data
- Measuring Bed Thickness
- Measured Sections
- Bed Orientation
- Correction for True Thickness
- Error of Bed Thickness Measurements
- Statistical Methods
In Holden crater bed thickness distributions were measured at ten continuous vertical sections in the interval identified by Grant et al. Changes in bed thickness with bed number (consecutive beds numbered within the stratigraphic section from bottom to top) for each section are presented in Figures 3.9-3.12. Bed thickness measurements were plotted in histograms where the frequency of bed thickness is normalized so that the total area in the histogram sums to 1 (Figure 3.13).
This test is appropriate when parameters must be estimated from data, as is the case for the bed thickness measurements here.
Results
- Holden Crater
- Bed Thickness Statistics
- Trends in Thickness versus Stratigraphic Position
- Bed Thickness Distributions
- Log-Log Plots
- Plateau West of Juventae Chasma
- Bed Thickness Statistics
- Trends in Thickness versus Stratigraphic Position
- Bed Thickness Distributions
- Log-Log Plots
- Gale Crater
- Bed Thickness Statistics
- Trends in Thickness versus Stratigraphic Position
- Bed Thickness Distributions
- Log-Log Plots
- Additional Sections
- Bed Thickness Statistics
- Trends in Thickness versus Stratigraphic Position
- Bed Thickness Distributions
- Log-Log Plots
Using the 1 meter per pixel orthoimages (Table 3.3), the number of beds is approximately half that measured with the 25 centimeter per pixel orthoimages, ranging from 23 beds (H5) to only 49 beds (H9). RAM and RUD results for the 1 m/pixel orthoimage sections are comparable to those for the 25 centimeter per pixel sections. The normal distribution is rejected for all eight storm sections using both the 25 centimeter per pixel and 1 meter per pixel orthoimages.
The Becquerel and Danielson sections contain the most beds, 339 and 158 respectively, measured at 25 centimeters per square meter. pixel orthoimage.
Discussion
- Bed Thickness on Mars
- Stratigraphic and Statistical Trends in Bed Thickness
- Thinning and Thickening Trends
- Statistical Distribution of Bed Thickness
- Power-Law Behavior of Bed Thickness Frequency
- Building a Global Inventory of Bed Thickness Distributions on Mars
- Challenges of Bed Thickness Analysis
RAM tests in Holden Crater on the plateau west of Juventae and Gale Crater reveal that bed thickness in these sections is not randomly distributed around the mean;. These results suggest that bed thickness distributions measured in Gale and on the western Juventae Plateau may be more consistent with stochastic sediment accumulation. 98 more ways in which depositional environments or mechanisms can be linked to unique bed thickness statistics in the future.
However, it is this uncertainty that necessitates bed thickness analyzes such as those presented in this study.
Conclusions
Representative exponential, lognormal, normal, and power law cumulative bed thickness distributions plotted on a linear scale. Bed thickness shown as a function of stratigraphic position for sections measured on the plateau west of Juventae Chasma. Bed thickness shown as a function of stratigraphic position for sections measured in Argyre Planitia, Athabasca Valles, Becquerel Crater, Candor Crater, Cross Crater, Eberswalde Crater, and Danielson Crater.
Log-log plot showing the proportion of bed thickness values greater than or equal to t for sections measured in Holden, West Juventae plateau, Gale, Argyre,.
Introduction
130 Clay and sulfate terrains are generally observed to be distinct spatially and likely temporally on the surface of Mars, a difference that is assumed to be indicative of global-scale changes in water chemistry and climatic conditions [Bibring et al., 2006]. Similarly, mono- and polyhydrate sulfates interbedded with kaolinite-bearing layers observed in the Columbus and Cross craters of the Terra Sirenum region suggest that clay and sulfate formation may have occurred simultaneously in acidic environments [Wray et al., 2011]. . Attempts have also been made to quantify the abundance of clay minerals from the orbital spectrum of the Martian surface [Poulet et al., 2008b; Poulet et al., 2009].
Although in situ measurements with the rover have shown that argillaceous and sulfate-bearing layers on Mars may contain additional clastic components such as olivine, pyroxene, and plagioclase [i.e. Vaniman et al., 2014], we chose to first study simple binary mixtures to test this. to blend.
Spectral Mixing Models
The fractional areas obtained can be converted to mass or volume fraction if the values for particle size and density of each component are known or assumed. As shown by Hapke [1993], it can be assumed that the average volume extinction efficiency, QE, is equal to unity for a mixture of closely packed particles, so the volume extinction coefficient, Ej, of component j is defined as: . Therefore, the weighting coefficients derived from (3) can be converted to mass fraction estimates if the solid density and particle diameter of each component are known.
The spectral weight coefficients are determined from the model fit and solid density values can be obtained from the literature, so it is only necessary to measure or assume a value for the ratio of particle diameters to Eq. to solve.
Materials and Methods
- Laboratory Measurements
- Analysis of Band Depths and Band Minima
- Linear (Checkerboard) and Nonlinear (Intimate)
- Modeling Mass Fraction
The three spectra were then averaged to produce a single spectrum representing a total of 600 scans for each mixture and each. Finite element spectral weighting coefficients of clay and epsomite were modeled for each mixture from the reflectance (checkerboard mixing model, Eq. 4)) spectra using linear least squares inversion with non-negativity constraint implemented in MATLAB using the lsqnonneg function. Linear least squares were performed for each mixture using the measured spectrum of the mixture and an input matrix containing the end-member spectra of the pure clay and epsomite of that batch and the positive and negative slope lines.
The optimized particle diameter ratio (Dclay:Depsomite) was calculated for each mixture according to Eq. 10), using nonlinear least squares (lsqnonlin function in MATLAB, www.mathworks.com/help/optim/ug/lsqnonlin.html) to minimize the sum of the squared residuals between the measured and modeled mass fraction values .
Results
- Spectral Observations
- Full Wavelength Range (1.25-2.6 µm) Model Results
- Measured Versus Modeled Spectra
- Measured and Optimized Particle Diameter Ratios
- Modeled Mass Fractions
- Relative Uncertainty of Model Fits
- Partial Wavelength Range (2.1-2.6 µm) Model Results
There are large differences between measured and modeled quantities, especially for mixtures containing ~50-80 wt% clay, where errors can be as high as 30-40 wt%. Clay mass fractions are consistently overmodeled (epsomite undermodeled) in the mixtures with a high clay content (50-95 wt. clay) and undermodelled in the mixtures with a low clay content (5-20 wt.). The relative uncertainties of the modeled mass fractions based on SSA spectra are comparable to uncertainties calculated from reflectance spectra (Figure 4.15d-f).
As was the case when using the full spectral range, clay mass fractions are generally overestimated for mixes with high clay abundances (>80 wt %) and underestimated for mixes with low clay abundance (Table 4.4).
Discussion
- Implications of Measured and Modeled Particle Size
- Checkerboard Versus Intimate Mixing Models
- Relevance for Quantifying Hydrated Minerals on Mars
NAu-2 nontronite and epsomite measured and modeled mixture spectra. a) Reflectance spectra of the nontronite NAu-2 mixture series shifted along the y-axis for clarity. SWa-1 nontronite and epsomite measured and modeled mixture spectra. a) Reflection spectra of the non-tronite SWa-1 mixture series shifted along the y-axis for clarity. SCa-3 montmorillonite and epsomite measured and modeled mixture spectra. a) Reflection spectra of the montmorillonite SCa-3 mixture series shifted along the y-axis for clarity.
SWy-2 montmorillonite and epsomite measured and modeled mixture spectra. a) Reflectance spectra of the montmorillonite SWy-2 mixture series shifted along the y-axis for clarity.
Conclusions
Introduction
Data and Methods
- Nodule Classification and Nomenclature
- MAHLI
- Mastcam
- APXS
- ChemCam
Shape and Size Distributions
- Solid Nodules
- Hollow Nodules
- Filled Nodules
- Statistical Testing
- Summary
Spatial Distribution
- Lateral Distribution
- John Klein Drill Site
- Cumberland Drill Site
- Raised Ridges and Nodules
- Vertical Distribution
- Selwyn
- Yellowknife Bay Egress
- Summary
Chemical Composition of the Sheepbed Nodules
- APXS
- ChemCam
- Summary of Geochemical Results
Discussion
- Petrogenesis of Sheepbed Nodules
- Controls on Nodules Shape and Size
- Controls on Nodule Spacing
- Growth of Solid Nodules
- Growth of Hollow Nodules
- Timing of Concretions Formation
- Nodules on Mars: Gale Crater vs. Meridiani Planum
- Concretions and the Preservation of Martian Organics
Conclusions
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