Characterizing Acoustic Emission Signals Throughout The Laboratory Seismic Cycle: Insights On Seismic Precursors
Open Access
- Author:
- Bolton, David
- Graduate Program:
- Geosciences
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- April 16, 2021
- Committee Members:
- Chris Marone, Chair & Dissertation Advisor
Charles Ammon, Major Field Member
Jacques Riviere, Outside Unit & Field Member
Mark Patzkowsky, Program Head/Chair
Donald Fisher, Major Field Member - Keywords:
- Earthquakes
Acoustic Emissions
Laboratory stick-slip experiments
Foreshocks - Abstract:
- Estimating the location and timing of future earthquakes has been a long-standing goal in earthquake seismology. However, progress in this area has been limited due to a poor understanding of earthquake nucleation and the connection between nucleation processes and precursory signals. For example, it is unclear why some earthquakes contain strong foreshock sequences, while others do not. In addition, it is not immediately clear how earthquake nucleation processes regulate the evolution of foreshocks and the causal processes that drive foreshock sequences are poorly constrained. In this dissertation, I seek to provide insights into some of these problems by using acoustic emissions (AEs) and laboratory stick-slip experiments, as proxies to foreshocks/seismic signals and tectonic earthquakes, respectively. In this dissertation, I use a variety of techniques to probe the pre-seismic and co-seismic properties of AE signals throughout the laboratory seismic cycle. A significant focus is devoted to understanding the parameter space and physical processes that control the temporal evolution of AE signals. To this end, I examine the effect of normal stress, shearing rate, and fault zone morphology on temporal variations in AE characteristics. In addition, I document co-seismic AE properties for both slow and fast laboratory earthquakes. The introduction lays out the motivation and broader implications of this work, particularly as it relates to earthquake nucleation processes and seismic precursors. In Chapter 2, I carry out an extensive analysis on event detection and answer basic questions surrounding the temporal variations in the Gutenberg-Richter b-value throughout the laboratory seismic cycle. Chapters 3-4 are focused on applying machine learning (ML) algorithms to study laboratory earthquakes. In Chapter 3, I use an unsupervised ML approach to characterize continuous AE data and identify precursors to lab earthquakes. In Chapter 4, I illuminate the driving processes that regulate the acoustic energy release throughout the seismic cycle by linking its temporal evolution to systematic changes in measured fault zone properties. In addition, Chapter 4 provides insights into ML-based predictions of laboratory earthquakes. Lastly, in Chapter 5 I focus on characterizing the AE radiation properties of slow and fast laboratory earthquakes. This work provides insights into acoustic signals and seismic precursors to laboratory earthquakes. The observations documented in this work provide an important framework for moving forward and should help guide future laboratory research in AE monitoring. In general, I show that laboratory earthquakes are often preceded by AE precursors and these precursors are modulated by fault slip rate and fault zone porosity. Lastly, I show that the acoustic radiation properties of slow and fast laboratory earthquakes are quite similar, which provides additional evidence that slow and fast events are controlled by similar physical processes.