Global map of surface winds on mars from barchan dune migration directions and horn asymmetry using a convolutional neural network.

Introduction: Windblown sand dunes are found on many planetary surfaces in the Solar System, including Venus, Earth, Mars, Titan, Pluto, and the 67P/Churyumov-Gerasimenko comet [1-3], constituting one of the most common landforms in the Solar System. Dune morphology evolves in concert with sediment availability and formative winds (e.g., barchan dunes form under relatively low sediment availability and unidirectional winds). Dunes are composed of a large sand volume relative to the volume of sand mobilized in a given transport event. Therefore, dune morphology reflects the integrated effect of a history of formative winds and sediment supply over relatively long (often > 1 kyr) timescales. Smaller superimposed bedforms, in turn, can provide indications of more recent winds or higher frequency (e.g., seasonal) events. Thus, bedform analyses provide an opportunity to better decipher planetary environments and their history [4]. As dunes collide with one another through time, dune patterns emerge through complex, self-organizing interactions [5–6]. It was previously shown that dunes with longer wavelengths, λ (m), display lower densities of dune defects (defined as the pair of terminations of a given dune) and interactions (defined as points where two dunes have collided or are within 10% of the mean wavelength of each other) [4,7]. As a result, dune interaction density offers a particularly promising avenue to evaluate the age or maturity of planetary dune fields. Several analyses of dune-field patterns have been conducted before. To date, the most commonly used metrics to quantify the abundance of dune interactions are the defect density, ρ (defined as the number of defect pairs per unit crest length, in m) [4] and the interaction density, I (number of interactions per unit area, in m) [7]. Both quantities were found to roughly scale with λ [7]; however, the latter trend naturally arises when the size of the region that interactions are counted in (or counting area) is correlated with wavelength – even if the average number of interactions per dune were to be invariant highlighting the need for a refined approach to quantifying the density of dune interactions. A new approach to quantifying dune interactions: We define the average number of interactions per dune within a dune field,

Quantifying dune interactions on planetary surfaces: updated methodology and implications for dune pattern analyses.

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Global map of surface winds on mars from barchan dune migration directions and horn asymmetry using a convolutional neural network.

Quantifying dune interactions on planetary surfaces: updated methodology and implications for dune pattern analyses.