A distinct ripple-formation regime on Mars revealed by the morphometrics of barchan dunes

Abstract

Sand mobilized by wind forms decimeter-scale impact ripples and decameter-scale or larger dunes on Earth and Mars. In addition to those two bedform scales, orbital and in situ images revealed a third distinct class of larger meter-scale ripples on Mars. Since their discovery, two main hypotheses have been proposed to explain the formation of large martian ripples—that they originate from the growth in wavelength and height of decimeter-scale ripples or that they arise from the same hydrodynamic instability as windblown dunes or subaqueous bedforms instead. Here we provide evidence that large martian ripples form from the same hydrodynamic instability as windblown dunes and subaqueous ripples. Using an artificial neural network, we characterize the morphometrics of over a million isolated barchan dunes on Mars and analyze how their size and shape vary across Mars’ surface. We find that the size of Mars’ smallest dunes decreases with increasing atmospheric density with a power-law exponent predicted by hydrodynamic theory, similarly to meter-size ripples, tightly bounding a forbidden range in bedform sizes. Our results provide key evidence for a unifying model for the formation of subaqueous and windblown bedforms on planetary surfaces, offering a new quantitative tool to decipher Mars’ atmospheric evolution.

Global Surface Winds and Aeolian Sediment Pathways on Mars From the Morphology of Barchan Dunes

“…Additionally, we identify two major surface wind convergence zones near the north pole using the dune orientations: (a) at the southern end of Chasma Boreale (labeled “f” in Figure 2b), and (b) between polar cyclonic and anti‐cyclonic flows (∼85°N,–120–120°E; Olympia Undae). In addition to enhanced sand flux, these wind convergence zones are likely responsible for the observed enhanced dune sizes and the lower density of isolated barchans relative to barchanoidal ridges in those regions, which tend to form in areas of greater sand supply (Rubanenko et al., 2022).…”

“…The sample is ∼10% larger than the sample used in Rubanenko et al. (2022) since we did not remove dunes with low detection accuracy (output of the CNN) of detection. Instead, we use the detection accuracy as average weights when computing the migration direction of the dune field, as explained below.…”

“…After manually reviewing a representative sample of the model detections, we elected to ignore any detections poleward of −70°N due to the presence of sublimation‐related features, such as spiders (Zuber, 2003), which the model occasionally misclassified as barchan dunes. Through this process, the CNN detected over a million individual isolated barchan dunes on Mars, which were subsequently filtered to increase the robustness of the results (Rubanenko et al., 2022; Supplementary Materials).…”

“…Following this filtering, our data set contained 737,521 dunes in 51,307 images. The sample is ∼10% larger than the sample used in Rubanenko et al (2022) since we did not remove dunes with low detection accuracy (output of the CNN) of The dune slipface is identified as the deepest convexity defect along the dune contour (e.g., d 1 > d 2 ), and horn lengths (L 1 and L 2 ) are measured as the distance between the slipface and the horn apexes. This example dune would be considered asymmetric if L 1 was twice as long as L 2 .…”

Automatic Characterization of Boulders on Planetary Surfaces From High‐Resolution Satellite Images

“…Mask R‐CNN is widely used in computer vision applications such as autonomous driving, robotics, and medical imaging (Fang et al., 2023; Jia et al., 2020; Johnson, 2018; Malbog, 2019; Shu et al., 2020). Moreover, it has previously been used for studying surface processes from satellite imagery, yielding sufficient accuracy in characterizing the morphometrics of various landforms (e.g., up to ∼80% mean average precision (AP) for planetary dunes and boulders; Rubanenko et al., 2021; 2022; 2023; Shimizu et al., 2022).…”