Nearest Neighbor Analysis of Volcanic Rootless Cones, Ice Mounds and Impact Craters on Earth and Mars

Mathematics – Logic

Scientific paper

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5480 Volcanism (6063, 8148, 8450), 5499 General Or Miscellaneous, 6225 Mars, 8428 Explosive Volcanism, 8485 Remote Sensing Of Volcanoes

Scientific paper

The objective of this research is to apply statistical methods to distinguish among different origins for small (less than a few km), circular to elongate, positive-relief mounds and cones on Earth and Mars. Geologic features that form in the presence of water or ice are of key interest because they provide information about the abundance and spatial distribution of volatiles through time. Such features include volcanic rootless cones, ice mounds (e.g., pingos), rampart craters and subglacial volcanoes. Recent high-resolution MOC and broader coverage THEMIS data of Mars have revealed numerous groups of small mounds, some of which have been suggested to be volcanic rootless cones and/or ice mounds. To date, we have statistically examined the spatial distributions of Icelandic rootless cones, ice mounds in Canada and Alaska, martian impact craters and martian mounds that have been interpreted as rootless cones and/or ice mounds. Using nearest neighbor (NN) methodology, we calculate R (the average distance between `nearest neighbors' within a group of features, scaled to that expected of a random spatial distribution). We find all terrestrial groups of rootless cones and ice mounds show intragroup aggregation (all R < 1), i.e., features are more closely spaced than would be expected from a random spatial distribution. With a single exception, NN analysis of the candidate martian rootless cones and ice mounds similarly yields R < 1, indicating that the spatial distributions of these candidate martian groups are consistent with both ice mound or rootless cone origins. The similarity of NN results for terrestrial rootless cones and ice mounds precludes discrimination between these two possible origins based on the R value alone, and we are currently exploring statistical methods to further characterize the spatial distributions. These NN results, however, can be used to distinguish these features from martian impact craters, all of which had R > 1, i.e., they are spaced further apart than would be expected from a random spatial distribution. Our goal is to develop NN analysis into a powerful remote sensing tool to distinguish geological features such as rootless cones, ice mounds and impact craters, especially when degradation or modification precludes identification based on morphology alone.

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