Teaching with Digital 3D Models of Minerals and Rocks

The disruption to geoscience curricula due to the COVID-19 pandemic highlights the difficulty of making mineral and rock samples accessible to students online rather than through traditional lab classes. In spring 2020, our community had to adapt rapidly to remote instruction; this transition amplified existing disparities in access to geoscience education but can be a catalyst to increase accessibility and flexibility in instruction permanently. Fortunately, a rich collection of 3D mineral and rock samples is being generated by a community of digital modelers (e.g., Perkins et al., 2019)

The Fold Illusion: The Origins and Implications of Ogives on Silicic Lavas

Folds on the surfaces of mafic lavas are among the most readily recognized geological structures and are used as first-order criteria for identifying ancient lavas on Earth and other planetary bodies. However, the presence of surface-folds on the surface of silicic lavas is contested in this study and we challenge the widely accepted interpretation that silicic lava surfaces contain folds using examples from the western United States and Sardinia, Italy. We interpret the ridges and troughs on their upper surfaces, typically referred to as ‘ogives’ or ‘pressure ridges’, as fracture-bound structures rather than folds. We report on the absence of large-scale, buckle-style folds and note instead the ubiquitous presence of multiple generations and scales of tensile fractures comparable to crevasses in glaciers and formed in ways similar to already recognized crease structures. We report viscosity data and results of stress analyses that preclude folding (ductile deformation in compression) of the upper surface of silicic lavas at timescales of emplacement (weeks to months). Therefore, analysis of fold geometry (wavelength, amplitude, etc.) is erroneous, and instead the signal produced reflects the strength and thickness of the brittle upper surface stretching over a ductile interior. The presence of ogives on the surfaces of lavas on other planetary bodies may help to elucidate their rheological properties and crustal thicknesses, but relating to their tensile strength, not viscosity

Independent, Semi-Automated Classification of Petrographic Features in Volcanic Rocks Using FiJi and Weka

Traditional methods of collecting quantitative petrographic data from thin sections (modal mineralogy, size distribution, shapes, etc.) are time- and labor-intensive, and rarely have sample sizes adequate to statistically describe complex rocks (i.e. volcanic rocks). Although manual counting and measurements are now routinely supplemented by digital image analysis, the majority of quantitative petrographic studies still go through a manual digitization stage where object classes are traced before further analyses. This is a major rate-limiting step that reproduces the same problems of small n-values resulting from significant effort. We have valuated the potential and limitations of using the Trainable Weka Segmentation (TWS) plugin within the commonly used ImageJ / Fiji digital image analysis and processing environment. Specifically, we have assessed their capacity to classify, segment, and threshold user-defined petrographic features from a suite of images of progressively more complex volcanic rocks to accelerate the collection of quantitative petrographic data. TWS uses a fast-random-forest algorithm to classify an image based on a set of training pixels selected by the user - in this case different mineral phases, vesicles, etc. Training of the classifier is intuitive and fast. For example, three classes each with eleven training spots are classified in less than 1 minute for a medium to high-resolution image. Eight plane polarized light photomicrographs with increasing crystallinity and complexity were classified (i.e. trained) and automatically segmented using TWS. Samples where the assigned classes have distinct, homogeneous RGB values and sharp boundaries are successfully classified with TWS. However, samples where the classes are heterogeneous but similar, as a result of alteration for example, are not adequately classified. Once classified, two major efficiency gains are possible: (1) the classifier can be saved and applied again to any similar sample, and (2) the segmented image is immediately available for thresholding in ImageJ / Fiji (i.e. separating into class-specific images) without manual tracing or cut-and-paste. The thresholded images can then be measured using the image analysis tools in ImageJ / Fiji (e.g., dimensions, area, circularity, long-axis orientation, etc.)