An official website of the United States government
Abstract Distributed Acoustic Sensing (DAS) is an emerging technology that converts optical fibers into dense arrays of strainmeters, significantly enhancing our understanding of earthquake physics and Earth's structure. While most past DAS studies have focused primarily on seismic wave phase information, accurate measurements of true ground motion amplitudes are crucial for comprehensive future analyses. However, amplitudes in DAS recordings, especially for pre‐existing telecommunication cables with uncertain fiber‐ground coupling, have not been fully quantified. By calibrating three DAS arrays with co‐located seismometers, we systematically evaluate DAS amplitudes. Our results indicate that the average DAS amplitude of earthquake signals closely matches that of co‐located seismometer data across frequencies from 0.01 to 10 Hz. The noise floor of DAS is comparable to that of strong‐motion stations but higher than that of broadband stations. The saturation amplitude of DAS is adjustable by modifying the pulse repetition rate and gauge length. We also demonstrate how our findings enhance the understanding of fiber‐optic seismology and its implications for natural hazard mitigation and Earth structure imaging and monitoring. Specifically, our results suggest that with proper settings, DAS can detectP‐waves from an M6+ earthquake occurring 10 km from the cable without saturation, indicating its viability for earthquake early warning. Through quantitative comparison and analysis, we also find that local ambient traffic noise levels strongly affect the quality of seismic interferometry measurement, which is a powerful tool for near‐surface imaging and monitoring. Our methodology and findings are valuable for future DAS experiments that require precise seismic amplitude measurements.
more »
« less
This content will become publicly available on August 1, 2026
Monitoring Spatiotemporal Seismic Velocity Changes Using Seismic Interferometry and Distributed Acoustic Sensing in Mexico City
Abstract Distributed Acoustic Sensing (DAS) offers a transformative solution for dense, high‐resolution seismic monitoring to address the challenges of traditional seismometers in urban seismic surveys. Here, we employ seismic interferometry of the ambient noise field and the trace stretching method to monitor seismic velocity variations in Mexico City. We present spatiotemporal variations in relative Rayleigh wave group velocity calculated over two frequency bands (0.4–1.2 Hz and 1.2–3.6 Hz) using DAS data collected over a year. To investigate these variations, we model the impacts resulting from the 2022 Mw7.6 earthquake, along with the effects of precipitation and temperature on the calculated in the 0.4–1.2 Hz frequency band, which is primarily dominated by the fundamental mode of the Rayleigh waves. Our results indicate that the earthquake‐induced velocity drop differs in certain fiber sections, likely due to their non‐linear soil behaviors and co‐seismic stress changes but without relation to the maximum local deformation registered during the earthquake. Additionally, our modeling indicates that the velocity changes are influenced by seasonal temperature variations, and the impact of precipitation is relatively minor, at least for the depth range (50 m) examined in this study. This study highlights the capability of DAS to enhance spatiotemporal monitoring in urban environments, providing valuable insights into both seismic and environmental responses.
more »
« less
- Award ID(s):
- 2022716
- PAR ID:
- 10647222
- Publisher / Repository:
- Wiley
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Solid Earth
- Volume:
- 130
- Issue:
- 8
- ISSN:
- 2169-9313
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
More Like this
-
-
Abstract Distributed acoustic sensing (DAS) is a recently developed technique that has demonstrated its utility in the oil and gas industry. Here we demonstrate the potential of DAS in teleseismic studies using the Goldstone OpticaL Fiber Seismic experiment in Goldstone, California. By analyzing teleseismic waveforms from the 10 January 2018 M7.5 Honduras earthquake recorded on ~5,000 DAS channels and the nearby broadband station GSC, we first compute receiver functions for DAS channels using the vertical‐component GSC velocity as an approximation for the incident source wavelet. The MohoP‐to‐sconversions are clearly visible on DAS receiver functions. We then derive meter‐scale arrival time measurements along the entire 20‐km‐long array. We are also able to measure path‐averaged Rayleigh wave group velocity and local Rayleigh wave phase velocity. The latter, however, has large uncertainties. Our study suggests that DAS will likely play an important role in many fields of passive seismology in the near future.more » « less
-
Abstract Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. However, its distinct characteristics, such as unknown ground coupling and high noise level, pose challenges to signal processing. Existing machine learning models optimized for conventional seismic data struggle with DAS data due to its ultra-dense spatial sampling and limited manual labels. We introduce a semi-supervised learning approach to address the phase-picking task of DAS data. We use the pre-trained PhaseNet model to generate noisy labels of P/S arrivals in DAS data and apply the Gaussian mixture model phase association (GaMMA) method to refine these noisy labels and build training datasets. We develop PhaseNet-DAS, a deep learning model designed to process 2D spatio-temporal DAS data to achieve accurate phase picking and efficient earthquake detection. Our study demonstrates a method to develop deep learning models for DAS data, unlocking the potential of integrating DAS in enhancing earthquake monitoring.more » « less
-
Abstract Distributed acoustic sensing (DAS) offers a cost effective, nonintrusive method for high-resolution near-surface characterization in urban environments where conventional geophysical surveys are limited or nonexistent. However, passive imaging with DAS in urban settings presents challenges such as strong diurnal traffic noise, nonlinear array geometry, and poor fiber coupling to the ground. We repurposed a dark fiber in Melbourne, Australia, into a 25 km DAS array that traces busy arterial roads, tram routes, and orthogonal sections. By employing noise cross correlation and array beamforming, we calculated dispersion curves and successfully inverted for a near-surface shear-wave velocity model down to 100 meters. Stationary seismic sources are maximized by selecting daytime traffic signals, thereby recovering surface waves and reducing interference from acoustic waves from man-made structures in the subsurface. Poorly coupled channels, which are linked to fiber maintenance pits, are identified through cross-correlation amplitudes. The dispersion curve calculation further considers the channel orientation to avoid mixing Rayleigh and Love waves. Using a trans-dimensional Markov chain Monte Carlo sampling approach, we achieved effective model inversion without a prior reference model. The resulting near-surface profile aligns with mapped lithology and reveals previously undocumented lithological variation.more » « less
-
Seismic imaging and monitoring of the near-surface structure are crucial for the sustainable development of urban areas. However, standard seismic surveys based on cabled or autonomous geophone arrays are expensive and hard to adapt to noisy metropolitan environments. Distributed acoustic sensing (DAS) with pre-existing telecom fiber optic cables, together with seismic ambient noise interferometry, have the potential to fulfill this gap. However, a detailed noise wavefield characterization is needed before retrievingcoherent waves from chaotic noise sources. We analyze local seismic ambient noise by tracking five-month changes in signal-to-noise ratio (SNR) of Rayleigh surface wave estimated from traffic noise recorded by DAS along the straight university campus busy road. We apply the seismic interferometry method to the 800 m long part of the Penn State Fiber-Optic For Environment Sensing (FORESEE) array. We evaluate the 160 virtual shot gathers (VSGs) by determining the SNR using the slant-stack technique. We observe strong SNR variations in time and space. We notice higher SNR for virtual source points close to road obstacles. The spatial noise distribution confirms that noise energy focuses mainly on bumps and utility holes. We also see the destructive impact of precipitation, pedestrian traffic, and traffic along main intersections on VSGs. A similar processing workflow can be applied to various straight roadside fiber optic arrays in metropolitan areas.more » « less
