COVID-19大流行期间城市级暗光纤DAS基础设施使用情况测量
City-scale dark fiber DAS measurements of infrastructure use during the COVID-19 pandemic
摘要：
在最近的COVID-19大流行期间，世界各地的政府官员命令公民在家中隔离，实时测量道路、医院、杂货店和其他公共基础设施的使用情况对于准确预测病毒感染率和为未来的政府决策提供信息至关重要。虽然移动电话的位置提供了一些关于社区活动的信息，但对整个城市的地面运动进行密集的分布式地球物理感测更完整，也更具有匿名性。在本文中，我们演示了连接到加利福尼亚州帕洛阿尔托下方电信电缆的光纤分布式声学传感（DAS）如何在COVID-19大流行爆发和随后的隔离期间捕获车辆产生的地震和大地测量信号。我们利用DAS应变测量的路基变形所造成的当地汽车和卡车在自动模板匹配检测算法来计算每天行驶的车辆数量超过两个月的时间周围的旧金山湾区避难所的地方秩序。使用Sand Hill Road上一家大型杂货店附近的一段光纤，我们发现订单后车辆数量立即减少了50%，但斯坦福医院附近的数据显示，由于正在进行的医院活动，变化要微妙得多。我们将DAS测量的信息与其他检疫响应指标进行比较，发现与Google和Apple使用手机数据报告的相对变化存在很强的相关性。
英文摘要：
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.
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