2020
DOI: 10.1029/2019je006218
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Compositional Heterogeneity of Impact Melt Rocks at the Haughton Impact Structure, Canada: Implications for Planetary Processes and Remote Sensing

Abstract: Connecting the surface expression of impact crater-related lithologies to planetary or regional subsurface compositions requires an understanding of material transport during crater formation. Here, we use imaging spectroscopy of six clast-rich impact melt rock outcrops within the well-preserved 23.5-Ma, 23-km diameter Haughton impact structure, Canada, to determine melt rock composition and spatial heterogeneity. We compare results from outcrop to outcrop, using clasts, groundmass, and integrated clast-ground… Show more

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Cited by 8 publications
(4 citation statements)
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“…Formulas for calculation of spectral parameters are given in Table A1. Formulas for the following step, aggregation of parameters to mineral indicators, are given in Table A2, following similar workflows to Greenberger et al ( , 2020. These provide mineral identifications and quantitative parameterizations of mineral occurrence, but occur-rence% should not be construed at this point as quantitative indication of wt% or vol%.…”
Section: Imaging Spectroscopymentioning
confidence: 99%
See 1 more Smart Citation
“…Formulas for calculation of spectral parameters are given in Table A1. Formulas for the following step, aggregation of parameters to mineral indicators, are given in Table A2, following similar workflows to Greenberger et al ( , 2020. These provide mineral identifications and quantitative parameterizations of mineral occurrence, but occur-rence% should not be construed at this point as quantitative indication of wt% or vol%.…”
Section: Imaging Spectroscopymentioning
confidence: 99%
“…Analysis of the imaging spectroscopy data was automated, was done on each pixel, and primarily occurred through calculation of spectral parameters (Clark & Roush, 1984;Pelkey et al, 2007;Viviano-Beck et al, 2014) for the presence or absence of key absorption features and then using those parameters to develop mineral indicators, similar to the workflow of Greenberger et al (2020. This approach was selected over pattern matching algorithms such as 216 spectral feature fitting (Clark, Gallagher, & Swayzel, 1990) or Tetracorder (Clark et al, 2003), which require spectral libraries containing every mineral within the image, including every solid solution composition. The large data volume combined with high spatial resolution of our data set permits analysis of individual grains.…”
Section: Imaging Spectroscopymentioning
confidence: 99%
“…A series of parameters were developed to distinguish minerals and other materials based on their major absorptions, exact band centers of those absorptions, and slopes/ratios where only part of the absorption was covered (Table 2; after Greenberger et al, 2020;Pelkey et al, 2007;Viviano-Beck et al, 2014). Parameters were then aggregated with thresholds highlighting the presence and/or absence of diagnostic features to map key materials within the image (Table 3).…”
Section: Spectral Parameters and Mineral Mappingmentioning
confidence: 99%
“…Typical tasks for HSI analysis of geological targets include classification, segmentation, anomaly detection, and unmixing [2] and use instruments that target the visible to mid-infrared wavelengths (VIS-MIR, ∼400-20000 nm) for mineral, ice, and atmospheric gas identification [9]. Semi-manual investigation is still common in these tasks [4,6,22]. One common approach for classification by expert spectral geologists is to apply knowledge of likely geologic processes occurring in the study target to isolate important known absorptions and use simple algebraic operations ("spectral parameters") to map relative abundances of materials.…”
Section: Introductionmentioning
confidence: 99%