Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866917401171329024 |
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| author | Cohen, Khen Nissan, Natanel Nissan, Ofir Lellouch, Ariel |
| author_facet | Cohen, Khen Nissan, Natanel Nissan, Ofir Lellouch, Ariel |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_10331 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing Cohen, Khen Nissan, Natanel Nissan, Ofir Lellouch, Ariel Geophysics Image and Video Processing Signal Processing Applied Physics Optics 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. |
| title | Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing |
| topic | Geophysics Image and Video Processing Signal Processing Applied Physics Optics |
| url | https://arxiv.org/abs/2604.10331 |