GSA Annual Meeting in Phoenix, Arizona, USA - 2019

Paper No. 131-1
Presentation Time: 9:00 AM-6:30 PM


SMITH, Zachary D., School of Earth Sciences, The Ohio State University, 125 S Oval Mall, Columbus, OH 43210, MAXWELL, David J., GIS Lab, Southern Utah University, 351 West University Boulevard, SC 302B, Cedar City, UT 84720, SMITH, Danielle C., Cedar City, UT 84720 and KAISER, Jason F., Department of Physical Science, Southern Utah University, 351 W University Blvd, Cedar City, UT 84720

Volcanic fields contain complex stratigraphic architecture that is difficult to document and describe using traditional field mapping methods. Developments of inexpensive unmanned aerial vehicles (UAVs) and photogrammetry software now allow these complex terrains to be mapped in high detail. The goal of this project is to use drones to collect high resolution imagery that can be used to statistically model and visualize changes in volcanism and depositional processes over time at the volcanic field scale. We use UAVs to obtain high resolution (2-7 mm pixel size) imagery of discrete locations across the southern margin of the Marysvale volcanic field (MVF) in southwest Utah, an area covering nearly 10,000 km2. The resulting photo arrays are imported into the photogrammetry software Agisoft PhotoScan (now Metashape) to generate 3D models and orthomosaics. Subsequently, we map characteristics of the outcrops such as grain size distributions of volcanic mudflows and block and ash-flows, fractures and faults, trace fossils, and features such as quenched blocks and lava flows in ArcGIS Pro for paleoenvironmental analysis. Numerical parameters are used to calculate statistics such as sorting, skewness, and kurtosis, as well as properties of individual flows including competence from which yield strength can be derived. Measurements, statistics, and properties of flows are stored in a GIS database that is linked to 3D points, lines, and polygons derived from tiled 3D models using unique keys. Statistics and properties of flows derived from in situ measurements can be easily replicated, as opposed to other grain size measurement methods involving unconsolidated sediment. Preliminary statistical analysis shows that grain size and sorting of volcanic mudflows and fluvial deposits is correlated to with eruption type, with smaller clast size and well-sorted deposits being associated with explosive volcanism. The GIS database produced for this project can be visualized and analyzed in both 2D and 3D for optimized statistical analysis of volcanic field architecture.