Abstract

Impact craters on the surface of Mars are degraded by erosion and infilling due to combinations of geological processes. These result in modifications of relative crater dimensions, including diameter increase and reduction of rim–floor depths. In principle, the longer a crater is exposed to geological processes, the more pronounced the modifications. Visualization and analysis of these effects are achieved by plotting the measured depths (M) of impact craters vs the corresponding theoretical depths (predicted: P) calculated from the crater diameters using depth/Diameter power laws. This type of diagram is referred to as MPD (measured depth vs predicted depth diagram). The advantage of using the MPD representation consists in the fact that the data plot along linear regressions, more easily interpreted than standard depth vs diameter diagrams.As an example of application of the method, the MPD was used to discriminate different generations of impact craters in Terra Sabaea into four groups: T0 (fresh craters), T1, T2 and T3 (from younger to older), all located on the most ancient geological unit in the area (Npld). Other units in the area are Hpl3 and Hr, impacted only by craters belonging to group T0, suggesting that these units are stratigraphically correlated. The data of 5 craters in superposition relationships with the eastern reaches of Evros Vallis, one of the major valley networks in the area, were plotted in the diagram and assigned each to a regression depending on the location of their data points in relation to the prediction bands of the regressions. The craters superposed to the valley all belonged to T0, indicating that Evros Vallis has the same relative age of units Hpl3 and Hr.A conceptual discussion of the results demonstrates that MPD statistics (a) are unaffected by the procedures used to acquire depths and diameters of impact craters and by the power laws used, and (b) can be interpreted irrespective of the sequence or combination of processes leading to modification of the crater morphometric data. These properties make the diagram a powerful statistical tool.

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