Recognition of Sedimentary Rock Occurrences in Satellite and Aerial Images of Other Worlds—Insights from Mars

Sedimentary rocks provide records of past surface and subsurface processes and environments. The first step in the study of the sedimentary rock record of another world is to learn to recognize their occurrences in images from instruments aboard orbiting, flyby, or aerial platforms. For two decades, Mars has been known to have sedimentary rocks; however, planet-wide identification is incomplete. Global coverage at 0.25–6 m/pixel, and observations from the Curiosity rover in Gale crater, expand the ability to recognize Martian sedimentary rocks. No longer limited to cases that are light-toned, lightly cratered, and stratified—or mimic original depositional setting (e.g., lithified deltas)—Martian sedimentary rocks include dark-toned examples, as well as rocks that are erosion-resistant enough to retain small craters as well as do lava flows. Breakdown of conglomerates, breccias, and even some mudstones, can produce a pebbly regolith that imparts a “smooth” appearance in satellite and aerial images. Context is important; sedimentary rocks remain challenging to distinguish from primary igneous rocks in some cases. Detection of ultramafic, mafic, or andesitic compositions do not dictate that a rock is igneous, and clast genesis should be considered separately from the depositional record. Mars likely has much more sedimentary rock than previously recognized.

A PCA-Based Framework for Determining Remotely Sensed Geological Surface Orientations and Their Statistical Quality

The orientations of planar rock layers are fundamental to our understanding of structural geology and stratigraphy. Remote sensing platforms including satellites, unmanned aerial vehicles, and Light Detection and Ranging scanners are increasingly used to build three-dimensional models of structural features on Earth and other planets. Remotely gathered orientation measurements are straightforward to calculate but subject to uncertainty inherited from input data, differences in viewing geometry, and the plane-fitting process, complicating geological interpretation. Here, we improve upon the present state of the art by developing a generalized means for computing and reporting errors in strike-dip measurements from remotely sensed data. We outline a general framework for representing the error space of uncertain orientations in Cartesian and spherical coordinates and develop a principal component analysis (PCA) regression method, which captures statistical errors independent of viewing geometry and input data structure. We also introduce graphical techniques to visualize the uniqueness and quality of orientation measurements and a process to increase statistical power by jointly fitting bedding planes under the assumption of parallel stratigraphy. These new techniques are validated by comparison of field-gathered orientation measurements with those derived from minimally processed satellite imagery of the San Rafael Swell, Utah, and unmanned aerial vehicle imagery from the Naukluft Mountains, Namibia. We provide software packages supporting planar fitting and visualization of error distributions. This method increases the precision and comparability of structural measurements gathered using a new generation of remote sensing techniques.