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We examine the coseismic influence of the 5 July 2019, MW7.1 Ridgecrest and the 24 June 2020 MW5.8 Owens Lake earthquakes on rockfall distributions in two undisturbed high-altitude areas of the southern Sierra Nevada Mountains, California, USA. These events occurred within the geologically recent (<2 Mya) Walker Lane/eastern California shear zone. While both study areas are characterized as plutonic, the Owens Lake event largely affected terrain that was formerly glaciated and oversteepened while the Ridgecrest event affected non-glaciated terrain. Our inventory of rockfall locations was derived from analysis of Sentinel-2 images acquired just prior to and immediately after the events. This difference mapping approach using readily-available Sentinel-2 imagery allows for rapid rockfall and landslide mapping. GIS analysis shows that even though the total area assessed for both earthquakes was similar (~1500 km2), the significantly lower magnitude Owens Lake event produced nearly twice as many (102) mappable rockslides as the significantly stronger Ridgecrest event (58), a difference likely due to slope oversteepening in the formerly glaciated area. Significant seismic amplification by topography and reactivation of preexisting failures was apparent for both areas. Inclusion of these factors may improve failure predictions and rockfall probability estimation. [ABSTRACT FROM AUTHOR]

Data analysis methods have scarcely kept pace with the rapid increase in Earth observations, spurring the development of novel algorithms, storage methods, and computational techniques. For scientists interested in Mars, the problem is always the same: there is simultaneously never enough of the right data and an overwhelming amount of data in total. Finding sufficient data needles in a haystack to test a hypothesis requires hours of manual data screening, and more needles and hay are added constantly. To date, the vast majority of Martian research has been focused on either one-off local/regional studies or on hugely time-consuming manual global studies. Machine learning in its numerous forms can be helpful for future such work. Machine learning has the potential to help map and classify a large variety of both features and properties on the surface of Mars and to aid in the planning and execution of future missions. Here, we outline the current extent of machine learning as applied to Mars, summarize why machine learning should be an important tool for planetary geomorphology in particular, and suggest numerous research avenues and funding priorities for future efforts. We conclude that: (1) moving toward methods that require less human input (i.e., self- or semi-supervised) is an important paradigm shift for Martian applications, (2) new robust methods using generative adversarial networks to generate synthetic high-resolution digital terrain models represent an exciting new avenue for Martian geomorphologists, (3) more effort and money must be directed toward developing standardized datasets and benchmark tests, and (4) the community needs a large-scale, generalized, and programmatically accessible geographic information system (GIS). [ABSTRACT FROM AUTHOR]

Transverse aeolian ridges (TARs) are unusual bedforms on the surface of Mars. TARs are common but sparse on Mars; TAR fields are small, rarely continuous, and scattered, making manual mapping impractical. There have been many efforts to automatically classify the Martian surface, but they have never explicitly located TARs successfully. Here, we present a simple adaptation of the off-the-shelf neural network RetinaNet that is designed to identify the presence of TARs at a 50-m scale. Once trained, the network was able to identify TARs with high precision (92.9%). Our model also shows promising results for applications to other surficial features like ripples and polygonal terrain. In the future, we hope to apply this model more broadly and generate a large database of TAR distributions on Mars. [ABSTRACT FROM AUTHOR]

Linear dunes and human fingerprints share many characteristics. Both have ridges, valleys, and defects (minutiae) in the form of bifurcations and termination of ridgeline features. For dunes, determining how defects vary across linear and transverse dunefields is critical to understanding the physics of their formative processes and the physical forcing mechanisms that produce dunefields. Unfortunately, manual extraction of defect locations and higher order characteristics (type, orientation, and quality) from remotely sensed imagery is both time-consuming and inconsistent. This problem is further exacerbated when, in the case of imagery from sensors in orbit around Mars, we are unable to field check interpretations. In this research, we apply a novel technique for extracting defects from multiple imagery sources utilizing a robust and well-documented fingerprint minutiae detection and extraction software (MINDTCT: MINutiae DecTeCTion) developed by the National Institute of Standards and Technology (NIST). We apply our 'fingerprinting' approach to Transverse Aeolian Ridges (TARs), relict aeolian features commonly seen on the surface of Mars, whose depositional and formative processes are poorly understood. Our algorithmic approach demonstrates that automating the rapid extraction of defects from orbitally-derived high-resolution imagery of Mars is feasible and produces maps that allow the quantification and analysis of these features. [ABSTRACT FROM AUTHOR]