arXiv subjects

Ariel Lellouch

Publications and source records attributed to Ariel Lellouch.

7 recordsLinked to original sources

arXiv2026

Distributed Acoustic Sensing for Urban Monitoring: Coverage Thresholds and Percolation

Distributed Acoustic Sensing (DAS) enables the repurposing of existing fiber-optic networks as ultra-dense, long-range seismic arrays for urban monitoring. However, constraints imposed by real-world fiber infrastructure topology and components limit its use for city-scale applications. Recent technological developments have paved the way for short-range, on-chip DAS. Assuming their availability, and based on a Graph Theory framework, we show that monitoring applications fall along a coverage spectrum with two critical thresholds that define three distinct regimes. Low coverage (<10%) can, with optimal design, resolve earthquake early warning, groundwater monitoring, geological mapping, and urban activity tracking. A percolation transition occurs at 51.6% coverage, beyond which the city effectively becomes fully covered and statistical traffic monitoring is possible. Only for effectively complete coverage, infrastructure monitoring, individual vehicle tracking, and pedestrian movement analysis become possible. Thus, privacy-related risks remain very low. We show and exemplify how, for metropolises around the world, an optimal sensing network can be designed for earthquake early warning, traffic monitoring, and urban activity tracking. This framework provides a near-future roadmap for deploying urban DAS networks as a backbone of smart city sensing.

cond-mat.stat-mech↗

arXiv2026

Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing

We present a scalable method for geolocalizing buried fiber-optic cables using Distributed Acoustic Sensing (DAS) and traffic-induced quasi-static seismic signals. Assuming access to one end of the fiber, the method fuses DAS measurements with vehicle trajectories obtained from either video tracking or vehicle-mounted GPS. The fiber geometry is estimated by minimizing the mismatch between the measured and physics-based synthetic strain-rate maps. The framework combines a matched-filter initialization with neural-network-based trajectory optimization, enabling robust convergence under realistic noise and trajectory-uncertainty conditions. Simulation and field experiments demonstrate sub-meter localization accuracy, often on the order of tens of centimeters, and strong agreement with manual calibration by tap-testing. This approach provides a practical tool for mapping poorly documented underground fiber infrastructure and for supporting urban sensing applications.

physics.geo-ph↗

arXiv2024

Training a Distributed Acoustic Sensing Traffic Monitoring Network With Video Inputs

Distributed Acoustic Sensing (DAS) has emerged as a promising tool for real-time traffic monitoring in densely populated areas. In this paper, we present a novel concept that integrates DAS data with co-located visual information. We use YOLO-derived vehicle location and classification from camera inputs as labeled data to train a detection and classification neural network utilizing DAS data only. Our model achieves a performance exceeding 94% for detection and classification, and about 1.2% false alarm rate. We illustrate the model's application in monitoring traffic over a week, yielding statistical insights that could benefit future smart city developments. Our approach highlights the potential of combining fiber-optic sensors with visual information, focusing on practicality and scalability, protecting privacy, and minimizing infrastructure costs. To encourage future research, we share our dataset.

physics.geo-ph↗

arXiv2022

Detection and characterization of microseismic events from fiber-optic DAS data using deep learning

Microseismic analysis is a valuable tool for fracture characterization in the earth's subsurface. As distributed acoustic sensing (DAS) fibers are deployed at depth inside wells, they hold vast potential for high-resolution microseismic analysis. However, the accurate detection of microseismic signals in continuous DAS data is challenging and time-consuming. We design, train, and deploy a deep learning model to detect microseismic events in DAS data automatically. We create a curated dataset of nearly 7,000 manually-selected events and an equal number of background noise examples. We optimize the deep learning model's network architecture together with its training hyperparameters by Bayesian optimization. The trained model achieves an accuracy of 98.6% on our benchmark dataset and even detects low-amplitude events missed during manual labeling. Our methodology detects more than 100,000 events allowing the reconstruction of spatio-temporal fracture development far more accurately and efficiently than would have been feasible by traditional methods.

physics.geo-ph↗

arXiv2020

Low-magnitude Seismicity with a Downhole Distributed Acoustic Sensing Array -- examples from the FORGE Geothermal Experiment

We show the capabilities of a downhole Distributed Acoustic Sensing (DAS) array in detecting, locating and characterizing low-magnitude earthquakes occurring in the vicinity of the Frontier Observatory for Research in Geothermal Energy (FORGE) site in Utah. 10.5 days of continuous data were acquired in a monitoring well at the FORGE geothermal site during the initial stimulation of an Enhanced Geothermal System in April-May 2019. Earthquake activity beneath Mineral Mountains, Utah also occurred within 10 km of the FORGE monitoring well. During the experiment, four events from those areas were cataloged by the University of Utah Seismograph Stations. Our processing of DAS data, including template matching, finds 82 earthquakes during that period, of which 16 are visible on the regional network. The magnitude of completeness obtained by DAS processing is better by at least M=0.5 than the dense surface array around the FORGE site. While a single vertical DAS array is limited in terms of event location due to its azimuthal ambiguity, multiple DAS wells or a combination of a downhole array with surface stations or near-surface horizontal DAS could jointly resolve locations. All detected events probably originated from the two active source areas and can be clustered into several distinct families.

physics.geo-ph↗

arXiv2020

Near-surface Characterization Using a Roadside Distributed Acoustic Sensing Array

Thanks to the broadband nature of the Distributed Acoustic Sensing (DAS) measurement, a roadside section of the Stanford DAS-2 array can record seismic signals from various sources. For example, it measures the earth's quasi-static distortion caused by the weight of cars (<0.8 Hz), and Rayleigh waves induced by earthquakes (<3 Hz) and by dynamic car-road interactions (3-20 Hz). We directly utilize the excited surface waves for shallow shear-wave velocity inversion. Rayleigh waves induced by passing cars have a consistent fundamental mode and a noisier first mode. By stacking dispersion images of 33 passing cars, we obtain stable dispersion images. The frequency range of the fundamental mode can be extended by adding the low-frequency earthquake-induced Rayleigh waves. Thanks to the extended frequency range, we can achieve better depth coverage and resolution for shear-wave velocity inversion. In order to assure clear separation from Love waves and aligning apparent velocity with phase velocity, we choose an earthquake that is approximately in line with the array. The inverted models match those obtained by a conventional geophone survey performed by a geotechnical service company contracted by Stanford University using active sources from the surface until about 50 meters. In order to automate the Vs inversion process, we introduce a new objective function that avoids manual dispersion curve picking. We construct a 2-D Vs profile by performing independent 1-D inversions at multiple locations along the fiber. From the low-frequency quasi-static distortion recordings, we invert for a single Poisson's ratio at each location along the fiber. We observe spatial heterogeneity of both Vs and Poisson's ratio profiles. Our approach is dramatically cheaper than ambient field interferometry and reliable estimates can be obtained more frequently as no lengthy cross-correlations are required.

physics.geo-ph↗

arXiv2020

City-scale dark fiber DAS measurements of infrastructure use during the COVID-19 pandemic

Throughout the recent COVID-19 pandemic when government officials around the world ordered citizens to quarantine inside their homes, real-time measurements about the use of roads, hospitals, grocery stores, and other public infrastructure became vital to accurately forecast viral infection rates and inform future government decisions. Although mobile phone locations provide some information about community-level activity, dense distributed geophysical sensing of ground motions across a city are more complete and also natively anonymous. In this paper, we demonstrate how fiber-optic Distributed Acoustic Sensing (DAS) connected to a telecommunication cable beneath Palo Alto, CA captured seismic and geodetic signals produced by vehicles during the COVID-19 pandemic outbreak and subsequent quarantine. We utilize DAS strain measurements of roadbed deformation caused by local cars and trucks in an automatic template matching detection algorithm to count the number of vehicles traveling per day over a two-month period around the timing of the San Francisco Bay Area shelter-in-place order. Using a segment of the optical fiber near a major grocery store on Sand Hill Road we find a 50% decrease in vehicle count immediately following the order, but data from near Stanford Hospital showed a far more subtle change due to on-going hospital activities. We compare the information derived from DAS measurements to other quarantine response metrics and find a strong correlation with the relative changes reported by Google and Apple using mobile phone data.

physics.geo-ph↗