<p dir="ltr">Studying changes in seismic velocities beneath two separate volcanic systems in the Aleutian arc. Focusing on eruptive behavior, this research delineates subsurface changes through the lens of changes in seismic velocity.</p>

<p dir="ltr">The Alpine Fault, located in the South Island, New Zealand, is a subject of intense geological study due to its potential to trigger large earthquakes. It encompasses a complex system with the interplay of mechanics, thermodynamics, and fluid. Gaining insights into these systems not only enhances our understanding of the fault but also holds the potential to guide risk mitigation efforts.</p><p dir="ltr">The damage extent and fracture networks within the metamorphic rock mass adjacent to the fault can be effectively characterized by seismic anisotropy, an elastic property of rock, where seismic waves travel at different speeds with variation directions. This thesis presents a comprehensive exploration of seismic anisotropy in the hanging wall immediately adjacent to the principal slip zone of the Alpine Fault in New Zealand. Leveraging the borehole seismic data from a unique scientific drilling project and advanced numerical modeling techniques, the ultimate goal is to invert and parameterize the bulk seismic anisotropy.</p><p dir="ltr">Motivated by these challenges, the thesis undertakes several key initiatives: The first effort focuses on gaining a comprehensive understanding of an innovative method for seismic measurement: Distributed Acoustic Sensing (DAS) – examining its operational principles, factors influencing observed wavelets, and how it contrasts with traditional point sensors for accurate interpretation. Subsequently, the research introduces the implementation of an open-source seismic wave solver designed for modeling elastic wave propagation in complicated anisotropic media. This solver is further optimized for computational efficiency with its performance rigorously benchmarked.</p><p dir="ltr">With this preparedness, the inversion is further facilitated by high-performance computing (HPC) and a deep-learning algorithm specifically designed for automatically picking transit times. The inverted bulk elastic constants, compared to the intact rock, reveal 28% to 35% reductions in qP-wave velocity, characterizing the damage due to mesoscale fracture. Further analysis sheds light on the existence of orthogonal fracture sets and an intricate geometrical arrangement that agree with the previous borehole image log. This represents an advancement in our ability to characterize and understand the geologic processes with seismic anisotropy.</p>

<p dir="ltr">As cameras increasingly populate urban environments for surveillance, the threat of data breaches and losses escalates as well. The rapid advancements in generative Artificial Intelligence have greatly simplified the replication of individuals’ appearances from video footage. This capability poses a grave risk as malicious entities can exploit it for various nefarious purposes, including identity theft and tracking individuals’ daily activities to facilitate theft or burglary.</p><p dir="ltr">To reduce reliance on video surveillance systems, this study introduces Urban Seismic Event Detection (USED), a deep learning-based technique aimed at extracting information about urban seismic events. Our approach involves synthesizing training data through a small batch of manually labelled field data. Additionally, we explore the utilization of unlabeled field data in training through semi-supervised learning, with the implementation of a mean-teacher approach. We also introduce pre-processing and post-processing techniques tailored to seismic data. Subsequently, we evaluate the trained models using synthetic, real, and unlabeled data and compare the results with recent statistical methods. Finally, we discuss the insights gained and the limitations encountered in our approach, while also proposing potential avenues for future research.</p>