arxiv: 2604.10331 · v1 · submitted 2026-04-11 · ⚛️ physics.geo-ph · eess.IV· eess.SP· physics.app-ph· physics.optics

Recognition: unknown

Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing

Khen Cohen , Natanel Nissan , Ofir Nissan , Ariel Lellouch

Authors on Pith no claims yet

Pith reviewed 2026-05-10 15:26 UTC · model grok-4.3

classification ⚛️ physics.geo-ph eess.IVeess.SPphysics.app-phphysics.optics

keywords distributed acoustic sensingfiber geolocalizationburied cablesstrain-rate mappingvehicle trajectoryDASurban sensingseismic signals

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The pith

Buried fiber-optic cables can be geolocalized to sub-meter accuracy using distributed acoustic sensing and vehicle trajectories.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a method to determine the precise underground location of buried fiber-optic cables by combining Distributed Acoustic Sensing measurements with data on vehicle movements above ground. It works by creating synthetic maps of expected strain rates from known vehicle paths and adjusting the assumed fiber position until the synthetic and actual measurements match closely. This is valuable because many fiber installations lack accurate maps, which hinders applications like earthquake monitoring or urban infrastructure management in cities. The approach uses an initial matched filter followed by neural network optimization to handle noise and uncertainties effectively. Experiments show it achieves localization errors often below one meter, matching results from manual testing methods.

Core claim

By fusing DAS strain-rate data with vehicle trajectories obtained from video or GPS, the fiber geometry is recovered through optimization that minimizes the difference between observed and physics-simulated strain-rate patterns along the cable, yielding sub-meter accuracy in both simulations and real-world tests that aligns with tap-test calibrations.

What carries the argument

Mismatch minimization between measured DAS strain-rate maps and physics-based synthetic maps generated from vehicle trajectories, initialized by matched filtering and refined via neural-network trajectory optimization.

Load-bearing premise

The approach depends on having one accessible end of the fiber and sufficiently accurate vehicle trajectory information from video tracking or GPS, along with the reliability of generating matching physics-based synthetic strain-rate data.

What would settle it

A direct comparison in a new field site where the fiber position is independently verified by excavation or high-precision surveying, checking if the estimated path deviates by more than one meter from the true location.

Figures

Figures reproduced from arXiv: 2604.10331 by Ariel Lellouch, Khen Cohen, Natanel Nissan, Ofir Nissan.

**Figure 1.** Figure 1: Diagram of the fiber localization method. (A) A driving car is continuously recorded by a camera or GPS, while a nearby DAS system measures [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗

**Figure 2.** Figure 2: Spatio-temporal strain rates of a point object moving at constant speed [PITH_FULL_IMAGE:figures/full_fig_p004_2.png] view at source ↗

**Figure 3.** Figure 3: Top: Calculated spatial width for varying horizontal (Y) and vertical [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗

**Figure 4.** Figure 4: Visualization of detected crossing point locations per frame, derived [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗

**Figure 6.** Figure 6: Effect of the number of anchors on localization error. Results are [PITH_FULL_IMAGE:figures/full_fig_p007_6.png] view at source ↗

**Figure 5.** Figure 5: Localization error as a function of spatial measurement noise. The [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗

**Figure 10.** Figure 10: Strain-rate map over the full DAS data, including channels that are [PITH_FULL_IMAGE:figures/full_fig_p008_10.png] view at source ↗

**Figure 9.** Figure 9: Localization accuracy as a function of Poisson ratio [PITH_FULL_IMAGE:figures/full_fig_p008_9.png] view at source ↗

**Figure 11.** Figure 11: Camera view of the street covering approximately 70 m. Naive [PITH_FULL_IMAGE:figures/full_fig_p009_11.png] view at source ↗

**Figure 14.** Figure 14: RANSAC regression between the projected vehicle coordinate [PITH_FULL_IMAGE:figures/full_fig_p012_14.png] view at source ↗

**Figure 13.** Figure 13: Example of per-frame channel assignments corresponding to Fig. 12. [PITH_FULL_IMAGE:figures/full_fig_p012_13.png] view at source ↗

**Figure 16.** Figure 16: Matched-filter–derived initializations at four fixed depths ( [PITH_FULL_IMAGE:figures/full_fig_p013_16.png] view at source ↗

**Figure 17.** Figure 17: High-order polynomial fit (5 th-degree) applied to the matched-filter initialization points (x ∗ ℓ , y∗ ℓ ). While not metrically accurate, the fitted curve qualitatively captures large-scale geometry such as the fiber’s lateral crossing beneath the street. X. FRENET-SERRET EQUATIONS In modeling physically realistic optical fibers, it is useful to impose smoothness constraints on fiber geometry. Two geom… view at source ↗

**Figure 18.** Figure 18: Sensitivity analysis at a depth of 1 m. ∂u ∂x = F x 2 (2v−1)(x 2+y 2+1)− √ x2+y2+1+12 −2x 2 √ x2+y2+1+1(2v−1)(x 2+y 2+1)+ √ x2+y2+1+1 + √ x2+y2+1+1 (x 2+y 2+1) (2v−1)(x 2+y 2+1)+ √ x2+y2+1+1 4πG√ x2+y2+1+12 (x2+y2+1) 5 2 (25) ∂u ∂y = F xy (2v−1)(x 2+y 2+1)− √ x2+y2+1+12 −2 √ x2+y2+1+1(2v−1)(x 2+y 2+1)+ √ x2+y2+1+1 4πG√ x2+y2+1+12 (x2+y2+1) 5 2 (26) ∂u ∂z = F x −(2v−1)(x 2+y … view at source ↗

**Figure 19.** Figure 19: GPS trajectory of the moving vehicle, together with hammer-hit [PITH_FULL_IMAGE:figures/full_fig_p014_19.png] view at source ↗

**Figure 21.** Figure 21: YOLOv11 bus tracks as a function of horizontal frame position and [PITH_FULL_IMAGE:figures/full_fig_p015_21.png] view at source ↗

**Figure 22.** Figure 22: Mean localization noise as a function of horizontal position [PITH_FULL_IMAGE:figures/full_fig_p015_22.png] view at source ↗

**Figure 23.** Figure 23: Mean localization noise as a function of horizontal position [PITH_FULL_IMAGE:figures/full_fig_p016_23.png] view at source ↗

read the original abstract

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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Desk Editor's Note private letter to a colleague

The paper gives a workable way to locate buried fibers from DAS signals triggered by traffic, with sub-meter results in their tests, but it hinges on having solid vehicle paths and forward models.

read the letter

The core contribution here is a pipeline that takes DAS strain-rate data from passing vehicles, fuses it with external trajectory info, and solves for the fiber's buried path. They start with a matched filter to get a rough guess, then refine the geometry via neural-net optimization to minimize mismatch with a physics-based synthetic map. That specific combo looks new compared to earlier DAS localization work, and the reported outcomes are concrete: simulations and field runs hit tens of centimeters accuracy, lining up with manual tap tests. The method is practical for urban settings where fiber records are incomplete, and it avoids needing access to both fiber ends. Credit to the authors for grounding the inversion in real forward modeling rather than pure data fitting. The assumptions are stated plainly—one-end access, usable vehicle tracks from video or GPS, and reliable strain-rate synthesis—and the stress test did not turn up hidden contradictions or circularity. That said, the validation still rests on a modest number of field cases, and performance under noisier trajectories or complex geology is not fully mapped out yet. Error bars and exclusion rules for the experiments would help, but nothing in the reported results suggests the sub-meter claim is overstated on its own terms. Readers in fiber-optic sensing or city-scale monitoring will find this directly useful; it is not a theoretical advance but a clear engineering step. The work shows honest engagement with the practical constraints and deserves referee time to check the details and suggest broader tests.

Referee Report

0 major / 4 minor

Summary. The manuscript presents a scalable method for geolocalizing buried fiber-optic cables using Distributed Acoustic Sensing (DAS) of traffic-induced quasi-static seismic signals. Assuming access to one end of the fiber, it fuses DAS measurements with vehicle trajectories from video tracking or vehicle-mounted GPS. Fiber geometry is recovered by minimizing mismatch between observed and physics-based synthetic strain-rate maps, via matched-filter initialization followed by neural-network trajectory optimization. Simulations and field experiments report sub-meter accuracy (often tens of centimeters) with strong agreement to manual tap-testing calibration.

Significance. If the reported accuracy holds, the work supplies a practical, physics-informed tool for mapping undocumented underground fiber infrastructure and supporting urban DAS applications. Strengths include the explicit use of independent trajectory data and forward modeling rather than purely data-driven fitting, the absence of free parameters in the core estimation, and direct validation against tap-testing. These elements make the approach reproducible and extensible beyond the specific experiments shown.

minor comments (4)

The abstract and introduction would benefit from a concise statement of the precise mismatch metric (e.g., L2 norm on strain-rate time series) used in the optimization objective.
Figure captions for the synthetic strain-rate maps should explicitly note the vehicle speed range, sampling rate, and noise model employed, to allow readers to assess sensitivity.
A short paragraph on failure modes (e.g., when trajectory uncertainty exceeds the reported levels or when multiple vehicles are present) would strengthen the discussion of practical applicability.
The neural-network architecture details (layer count, activation functions, and regularization) are referenced but not tabulated; adding these in an appendix would improve reproducibility.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the positive and accurate summary of our work, the recognition of its strengths in using physics-based forward modeling with independent trajectory data, and the recommendation for minor revision. We are pleased that the sub-meter accuracy, reproducibility, and potential for urban DAS applications were highlighted.

Circularity Check

0 steps flagged

No significant circularity in derivation chain

full rationale

The paper's core method estimates buried fiber geometry via optimization that minimizes mismatch between observed DAS strain-rate signals and independent physics-based synthetic strain-rate maps generated from external vehicle trajectory data (video tracking or GPS). This forward-modeling step uses separate inputs and is validated against manual tap-testing calibration, without any self-definitional reduction, fitted parameters renamed as predictions, or load-bearing self-citations that collapse the result to the paper's own definitions. The derivation remains self-contained against external benchmarks and does not exhibit any of the enumerated circularity patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on standard domain assumptions in DAS and seismic modeling plus the availability of external trajectory data; no free parameters or invented entities are explicitly introduced in the abstract.

axioms (1)

domain assumption Physics-based synthetic strain-rate maps generated from vehicle trajectories accurately represent the measured DAS signals under realistic noise conditions.
This assumption underpins the mismatch minimization step described in the abstract.

pith-pipeline@v0.9.0 · 5441 in / 1296 out tokens · 29172 ms · 2026-05-10T15:26:10.105394+00:00 · methodology

discussion (0)

Reference graph

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