Chris McCaslin Dissertation Defense

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”

Abstract: Quantifying the mechanical properties of snow is important for various applications, such as assessing slope stability, vehicle mobility on snow-covered terrain, and understanding snowpack evolution. To understand this, we developed a novel non-contacting ultrasound laser system that gathers data from 10s to 100s of kHz in a controlled cold lab environment. We have access to two different laser ultrasound systems from Polytec – the PSV-400 Scanning Vibrometer and the VibroFlex Fiber. We compare these two lasers and present our new laser ultrasound system. We validate our observations using elastic wavefield modeling, comparison to previous studies, and other accredited snow property measurement systems such as the SnowMicroPen (SMP). Our findings indicate that the PSV-400 Scanning Vibrometer produces higher quality ultrasonic wavefield observations compared to the VibroFlex Fiber when sampling granular mixes in the cold lab. We also study the relationship between observed velocity changes and changing density during compaction experiments using the PSV-400. Additionally, we control the microstructure of various snow samples to better understand the observed ultrasonic wavefield datasets. By controlling supersaturation and temperature, different snow crystals are created within the cold lab. We periodically record the ultrasonic wavefield over 72 hours to observe wave propagation with snow metamorphism due to sintering. We present the observations of ultrasonic waves in various snow samples composed of different snow crystal classifications over time. We also investigate the quantified mechanical properties using cross validation from elastic waveform prediction, comparison to previous studies, and accredited snow property measurement instruments such as the SMP and MicroCT scans. Next, we discuss the relationship between observed velocity changes and snow crystal types. We hypothesize that crystal types influence the bulk mechanical properties observed through the ultrasonic wavefields. These ultrasonic datasets will be used to validate phase velocities from seismic data in the field, specifically Bogus Basin in Idaho. In April 2022, we acquired an active-source seismic time-lapse dataset at the Bogus Basin research site to capture changing seismic signals through the snowpack. Our survey included both surface and body wave measurements, and we used physical and empirical relationships to estimate velocity changes through storm and snow melt cycles. The adjacent SNOTEL station provided measurements of snow depth, snow water equivalent (SWE), and snow bulk densities during our survey. We utilized an electric hammer seismic source and recorded the seismic signals using geophones planted in the soil prior to snowfall. Our baseline seismic survey provided velocity gradient estimates for soil and regolith, while surface wave dispersion provided estimates for shear-wave velocities. In contrast, empirical snow velocity estimates suggested higher velocities for both p-waves and s-waves. During our time-lapse survey, we observed seismic “snow phases” that changed with snow density and thickness. We compared our survey results with poroelastic model data and full waveform simulations to validate seismic velocity changes and infer snow properties within the snowpack. Our approach and measurements demonstrate the feasibility of remotely monitoring the mechanical properties of seasonal snow using active source seismology.

Advisor: Dylan Mikesell, HP Marshall

Committee Members: Ellyn Enderlin, Lee Liberty