Multi-Disciplinary Applications of Oceanographic Geophysical Data Collection

Thesis (Ph.D.)--University of Washington, 2016-08

Geophysical data and methods are a key source for information about geologic features beneath the seafloor that are difficult to sample directly. Our knowledge of the Earth’s structure has largely relied on our ability to apply classical physics to study the Earth’s through the transmission of seismic and electromagnetic waves. As our data collection capabilities have benefited from technological advancements in connectivity, bandwidth, power usage, battery life and data storage, the scope of questions that can be addressed using seismology and other techniques is broadening. Larger data sets and increased bandwidth offer opportunities to explore multiple questions with individual data streams. This dissertation explores using seismology and other sources of time series data both as tools for exploring novel science questions but also as tools for teaching Earth science to students as they develop Science, Technology, Engineering, and Mathematics (STEM) skills. These analyses (1) create a model of crustal thickness and lower crustal velocities for crustal ages of 0.1-1.2 Ma on the Endeavour Segment of the Juan de Fuca Ridge by inverting travel times of crustal paths and non-ridge-crossing wide-angle Moho reflections obtained from a three-dimensional tomographic experiment; (2) use fin whale calls recorded by a seafloor seismic network on the Endeavour segment of the Juan de Fuca Ridge to create over 150 whale tracks using new techniques and identify four characteristic inter-pulse intervals (IPIs) that indicate group size and swimming speed and direction; and (3) engage students in analysis of data collected by networks of environmental sensors, which are used to study various natural phenomena, such as nutrient loading, climate change, and stream discharge to compare approaches to implementation in an undergraduate time-series analysis course. These results demonstrate the utility of seafloor networks as both instruments of primary data collection and teaching tools.