埋藏光纤地理定位与分布式声学传感
Buried Fiber-Optic Geolocalization with Distributed Acoustic Sensing
AI总结：
本文提出一种利用分布式声学传感和交通诱导准静态地震信号对埋藏光纤进行地理定位的可扩展方法，通过融合车辆轨迹数据实现亚米级精度的定位。
摘要：
我们提出了一种可扩展的方法，用于利用分布式声学传感（DAS）和交通诱导的准静态地震信号对埋藏光纤电缆进行地理定位。假设可以访问光纤的一端，该方法融合DAS测量与通过视频跟踪或车载GPS获得的车辆轨迹。光纤几何形状通过最小化测量值与基于物理的合成应变率地图之间的不匹配来估计。该框架结合了匹配滤波初始化与基于神经网络的轨迹优化，能够在现实噪声和轨迹不确定性条件下实现稳健收敛。模拟和实地实验显示了亚米级的定位精度，通常在十厘米范围内，并且与手动校准的敲击测试有很强的一致性。该方法为绘制 poorly documented 的地下光纤基础设施以及支持城市传感应用提供了实用工具。
英文摘要：
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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