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Advancing Urban Seismic Imaging and Monitoring Using Distributed Acoustic Sensing

Year: 2025
Type: dissertation
Abstract: Seismic hazards pose significant risks to urban areas, especially those built on soft sediments that amplify ground motion. Accurate hazard assessment and mitigation require dense, high-resolution imaging and continuous monitoring of the shallow subsurface. However, conventional seismic networks are often limited in cities due to high costs, sparse coverage, and logistical constraints, hindering efforts to characterize site effects and monitor changes over time. Distributed Acoustic Sensing (DAS) offers a transformative solution by converting existing fiber-optic telecom cables into dense seismic arrays. DAS enables scalable, cost-effective, and non-invasive seismic data acquisition with meter-scale spatial resolution, making it ideal for urban environments. However, challenges remain. Telecom fibers are not designed for geophysical monitoring, and uncertainties about their layout, coupling, and burial depth can degrade data quality. DAS also tends to have lower signal-to-noise ratios than traditional geophones and is directionally sensitive, detecting strain only along the fiber axis, which may limit wavefield capture. This thesis addresses these challenges through three investigations that enhance DAS's utility for urban seismic imaging and monitoring. These studies bridge the gap between DAS’s theoretical potential and practical urban applications, providing a framework for using telecom fiber networks as long-term seismic observatories in cities. Chapter 2 explores the shallow shear-wave velocity (Vs) structure in Granada, Spain—a region of moderate seismicity but high hazard due to shallow sediments. Using a two-day DAS deployment (August 26-27, 2020) along a telecom cable with complex geometry, we address urban noise and short recording duration. An ad-hoc processing scheme using frequency-wavenumber filtering improves virtual shot gathers and multi-mode dispersion images from ambient noise. Several Vs profiles are derived, revealing a shallow low-velocity layer (150-300 m/s) linked to alluvial deposits, transitioning to weathered metamorphic bedrock (>800 m/s). These results align with local geology and demonstrate DAS’s value for soil characterization and seismic microzonation. Chapter 3 investigates the near-surface Vs structure in the hill zone of Mexico City through joint inversion of Rayleigh and Love wave dispersion curves from DAS ambient noise. We first identify optimal channel pairs for capturing both wave types, then extract dispersion curves and invert them using a partial Markov Chain Monte Carlo approach to 300 m depth. The joint inversion enhances resolution of subsurface interfaces near 25 m depth—poorly resolved by single-wave inversions. Love waves constrain shallow layers (0-100 m), while Rayleigh waves inform deeper structures, resulting in a robust and stable model. This highlights DAS’s potential for fine-scale imaging in complex urban environments. Chapter 4 utilizes DAS to monitor spatiotemporal seismic velocity changes in Mexico City’s lacustrine basin over one year. Using seismic interferometry and the trace stretching method, we compute Rayleigh wave group velocity variations (dU/U) in two frequency bands (0.4-1.2 Hz and 1.2-3.6 Hz). We assess effects of the 2022 Mw7.6 earthquake, temperature, and precipitation on dU/U in the lower frequency band dominated by fundamental-mode Rayleigh waves. The earthquake caused spatially variable velocity reductions tied to nonlinear soil behavior and coseismic stress. Seasonal temperature fluctuations explain a significant portion of velocity variability in the shallow subsurface (<∼50 m), while precipitation effects are negligible. This study shows DAS’s unique ability to track both seismic and environmental subsurface responses.
DOI: 10.7302/27180
Author: Li, Yang
Language: English
Subfield: Geophysics
Open Access status: green
OpenAlex ID: W4414739401
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