A Novel Approach to Impact Crater Mapping and Analysis on Enceladus, Using Machine Learning

Abstract

AbstractImpact cratering is one of the most important processes shaping planetary surfaces, offering valuable clues about the target body's geologic history and composition. However, crater mapping has historically been done manually, a process that has proven to be both arduous and time consuming. This paper outlines a machine learning crater mapping approach for bodies with limited elevation data available (Digital Elevation Models). We applied a Convolutional Neural Network for the detection and morphometry of impact craters on Saturn's moon Enceladus using light‐shadow labels trained on data from the Cassini Imaging Science Subsystem. Our algorithm identified a total of 5,240 features which were used to quantify crater distribution; this included the highest number of small craters (<1–2 km in diameter) recorded on Enceladus by any previous published study. The pool of features was later down‐selected to craters between 0 and 30°N (latitude) imaged at high incidence (>60°) and phase angles (>26°). The down selection was necessary to accurately perform diameter measurements and derive depths from shadow estimation techniques to calculate depth–diameter ratios (d/D); a well‐studied relationship used to constrain planetary surface properties. Results show that the d/D ratio of craters in the equatorial region of Enceladus range from ∼0.06 to 0.37, with a median of 0.19. Our results will inform efforts to constrain the surface properties of this region of Enceladus, potentially also supporting future mission concept design for the Saturnian moon. Future work will explore the simple‐to‐complex crater transition and differences between this area's d/D and Enceladus' northern and southern latitudes.