用震动信号定位埋地光缆，精度达十厘米级。
Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing
- 结合振动数据与车辆轨迹，通过物理模型反推光缆位置
- 仿真与实测均实现亚米级定位，多数情况在十厘米内
- 适合城市地下光缆普查和智能感知应用
我们提出一种可扩展的埋地光纤电缆地理定位方法，利用分布式声学传感（DAS）与交通引起的准静态地震信号。假设仅能访问光纤一端，该方法融合DAS测量数据与来自视频追踪或车载GPS的车辆轨迹信息，通过最小化实测应变率图与物理模型生成的合成应变率图之间的差异来估计光纤几何形态。框架结合匹配滤波初始化与神经网络驱动的轨迹优化，可在真实噪声和轨迹不确定性条件下实现稳健收敛。仿真与野外实验表明，定位精度达到亚米级，通常在数十厘米量级，并与人工敲击校准结果高度一致。该方法为缺乏完整记录的地下光纤基础设施测绘及城市感知应用提供了实用工具。
原文摘要 · Abstract (English)
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.
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