Graduate Defense: James McCaslin

Dissertation Information

Title: On The Use Of Elastic Waves To Study Snow Microstructure And Mechanical Properties: A Look From The Lenses Of Laser Ultrasound And Time-Lapse Seismic

Program: Doctor of Philosophy in Geophysics

Advisor: Dr. Hans-Peter Marshall, Geosciences

Committee Members: Dr. Dylan Mikesell, College of Arts and Sciences; Dr. Ellyn Enderlin, Geosciences; and Lee Liberty, M.S., Geosciences

Abstract

Accurate knowledge of the mechanical and microstructural properties of snow is essential to improve snow science and engineering applications. In this dissertation, I investigated the use of ultrasound to quantify certain snow properties with a non-contacting laser ultrasound system (LUS), then I employed the knowledge gained from a small controlled lab-based method to a larger field-based active-source seismic method. I first evaluated two different LUS devices to determine which one produced more accurate ultrasound observations in analog snow (i.e. shaved ice). The PSV-400 Scanning Vibrometer was found to provide higher quality ultrasound observations compared to the VibroFlex Fiber Vibrometer.Using the PSV-400, I then investigated the relationship between snow compression-wave speed (Vp) and two snow properties: density and crystal type. Snow density was controlled by adjusting the volume of the analog snow samples while keeping the mass constant.

Different snow crystals were created in the cold lab using a snow maker. To validate the data in my different experiments, results were compared to previous studies, and waveform modeling was performed. Additionally, density measurements were made using a snow micro penetrometer (SMP). Crystal types were characterized by shape and size, as well as specific surface area (SSA). With these experiments, I showed that the LUS could be used to infer snow density from Vp using a poro-elastic constitutive model. I also showed through 72 hours of time-lapse LUS that snow sintering could be linked to increasing elastic moduli, with the rate of change and the maximum moduli being sensitive to snow crystal type. This experiment was conducted on four snow crystal types and at one temperature (-20 °C). I also found a relationship between SSA and the increasing elastic moduli with time. Taking the knowledge gained by these laboratory experiments, we then investigated a time-lapse active seismic dataset to investigate changes in this mountain snowpack with time.

In April 2022, an active-source seismic time-lapse survey was conducted at the Bogus Basin research site. An electric hammer seismic source and geophones were used to record seismic signals in the snowpack over a 24-channel vertical linear geophone array at the ground surface (i.e., below the snowpack). Surface and body seismic waves were observed throughout April during snowfall and snow melt events, and velocity changes were estimated using a combination of physical and empirical relationships. Snow depth, snow water equivalent (SWE), and snow bulk densities were measured at a nearby SNOTEL station to ensure accuracy. Distinct seismic “snow phases” were observed during the time-lapse survey, corresponding to variations in snow density and thickness. The survey results were compared to poro-elastic model data to verify changes in seismic velocity and identify velocity changes for snow accumulation, snow densification, and snow melt. The study successfully demonstrated the remote monitoring of mechanical properties of seasonal snow using active source seismology.