Identification of volcanic rootless cones, ice mounds, and impact craters on Earth and Mars: Using spatial distribution as a remote sensing tool

Details Identification of volcanic rootless cones, ice mounds, and impact craters on Earth and Mars: Using spatial distribution as a remote sensing tool Identification of volcanic rootless cones, ice mounds, and impact craters on Earth and Mars: Using spatial distribution as a remote sensing tool

Jun 2006

adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2006jgre..11106017b&link_type=abstract

Journal of Geophysical Research, Volume 111, Issue E6, CiteID E06017

Physics

14

Volcanology: Eruption Mechanisms And Flow Emplacement, Tectonophysics: Planetary Volcanism (5480, 8450), Volcanology: Remote Sensing Of Volcanoes

Scientific paper

This study aims to quantify the spatial distribution of terrestrial volcanic rootless cones and ice mounds for the purpose of identifying analogous Martian features. Using a nearest neighbor (NN) methodology, we use the statistics R (ratio of the mean NN distance to that expected from a random distribution) and c (a measure of departure from randomness). We interpret R as a measure of clustering and as a diagnostic for discriminating feature types. All terrestrial groups of rootless cones and ice mounds are clustered (R: 0.51-0.94) relative to a random distribution. Applying this same methodology to Martian feature fields of unknown origin similarly yields R of 0.57-0.93, indicating that their spatial distributions are consistent with both ice mound or rootless cone origins, but not impact craters. Each Martian impact crater group has R >= 1.00 (i.e., the craters are spaced at least as far apart as expected at random). Similar degrees of clustering preclude discrimination between rootless cones and ice mounds based solely on R values. However, the distribution of pairwise NN distances in each feature field shows marked differences between these two feature types in skewness and kurtosis. Terrestrial ice mounds (skewness: 1.17-1.99, kurtosis: 0.80-4.91) tend to have more skewed and leptokurtic distributions than those of rootless cones (skewness: 0.54-1.35, kurtosis: -0.53-1.13). Thus NN analysis can be a powerful tool for distinguishing geological features such as rootless cones, ice mounds, and impact craters, particularly when degradation or modification precludes identification based on morphology alone.

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