Matching Items (2)
Filtering by

Clear all filters

Rock Traits from Machine Learning: Application to Rocky Fault Scarps

Description
Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at fault scarps. Fault scarps indicate the structure of fault zones…

Rock traits (grain size, shape, orientation) are fundamental indicators of geologic processes including geomorphology and active tectonics. Fault zone evolution, fault slip rates, and earthquake timing are informed by examinations of discontinuities in the displacements of the Earth surface at fault scarps. Fault scarps indicate the structure of fault zones fans, relay ramps, and double faults, as well as the surface process response to the deformation and can thus indicate the activity of the fault zone and its potential hazard. “Rocky” fault scarps are unusual because they share characteristics of bedrock and alluvial fault scarps. The Volcanic Tablelands in Bishop, CA offer a natural laboratory with an array of rocky fault scarps. Machine learning mask-Region Convolutional Neural Network segments an orthophoto to identify individual particles along a specific rocky fault scarp. The resulting rock traits for thousands of particles along the scarp are used to develop conceptual models for rocky scarp geomorphology and evolution. In addition to rocky scarp classification, these tools may be useful in many sedimentary and volcanological applications for particle mapping and characterization.
ContributorsScott, Tyler (Author) / Arrowsmith, Ramon (Thesis advisor) / Das, Jnaneshwar (Committee member) / DeVecchio, Duane (Committee member) / Arizona State University (Publisher)
Created2020
168496-Thumbnail Image.png

Assessing Non-Rainfall Moisture Sources Through Relative Humidity and Soil Moisture in Dryland Regions

Description
Drylands make up more than 45% of the Earth’s land surface and are essential to agriculture and understanding global carbon and elemental cycling. This thesis presents an analysis of atmospheric relative humidity (RH) and temperature (T) as they impact soil moisture and water content at two dryland sites. In particular,…

Drylands make up more than 45% of the Earth’s land surface and are essential to agriculture and understanding global carbon and elemental cycling. This thesis presents an analysis of atmospheric relative humidity (RH) and temperature (T) as they impact soil moisture and water content at two dryland sites. In particular, this thesis assesses the likelihood and impact of non-rainfall moisture (NRM) sources on dryland soils. This work also includes a discussion of the development and testing of a novel environmental sensing network, using custom nodes called EarthPods, and recommendations for the collection of future data from dryland sites to better understand NRM events in these regions. An analysis of weather conditions at two drylands sites suggest that nighttime RH is frequently high enough for NRM events to occur. Thesis results were unable to detect changes in soil water content based on historical weather data, likely due to instrument limitations (depth and sensitivity of soil moisture probes) and the small changes in soil moisture during NRM events. However, laboratory tests of EarthPod soil moisture sensors indicated strong sensitivity to T. Characterization of these T sensitivities provide opportunities to calibrate and correct soil moisture estimates using these sensors in the future. This work provides the foundation for larger biogeochemical sampling campaigns focusing on NRM in dryland systems.
ContributorsHanan, Desmond (Author) / Trembath-Reichert, Elizabeth (Thesis advisor) / Das, Jnaneshwar (Committee member) / Throop, Heather (Committee member) / Arizona State University (Publisher)
Created2021