利用现有光纤网络进行城市传感

Jingxiao Liu, Haipeng Li, Hae Young Noh, Paolo Santi, Biondo Biondi, Carlo Ratti
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摘要

对城市震源的分析为了解城市环境提供了宝贵的信息,包括地震灾害、基础设施状况、人类流动性和文化生活。然而,由于成像高频人为震源所需的超密集地震阵列成本过高,传统地震传感网络无法在城市范围内对震源进行精确探测和定位。在这里,我们利用现有的光纤网络作为分布式声学传感系统,准确定位城市震源,并估算其强度随时间的变化情况。我们的方法克服了城市地震传感的距离限制,实现了对城市活动产生的远程震源的精确定位。检测到的活动包括车辆行驶、建筑工地和学校的作业。我们还展示了 SSP 值与噪声级测量值以及各种持久性城市特征(包括兴趣点密度、土地使用模式和人口统计)之间的强相关性。我们的研究展示了如何将时空 SSP 地图转化为有效捕捉城市动态的多特征城市数据,从而为将光纤网络用作无所不在的通用城市传感平台开辟了道路,并在城市和环境研究中得到广泛应用。
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Urban Sensing Using Existing Fiber-Optic Networks
The analysis of urban seismic sources offers valuable insights into urban environments, including seismic hazards, infrastructure conditions, human mobility, and cultural life. Yet, accurate detection and localization of seismic sources at the urban scale with conventional seismic sensing networks is unavailable due to the prohibitive costs of ultra-dense seismic arrays required for imaging high-frequency anthropogenic seismic sources. Here, we leverage existing fiber-optic networks as a distributed acoustic sensing system to accurately locate urban seismic sources and estimate how their intensity varies with time. By repurposing a 50-kilometer telecommunication fiber into an ultra-dense seismic array with 50,000 channels, we generate high-resolution spatiotemporal maps of seismic source power (SSP) across San Jose, California. Our approach overcomes the proximity limitations of urban seismic sensing, enabling accurate localization of remote seismic sources generated by urban activities. Examples of detected activities are vehicle movements and operations at construction sites and schools. We also show strong correlations between SSP values and noise level measurements, as well as various persistent urban features, including point of interest density, land use patterns, and demographics. Our study shows how spatiotemporal SSP maps can be turned into novel urban data that effectively captures urban dynamics across multiple features, thus opening the way towards the use of fiber-optic networks as a ubiquitous and general-purpose urban sensing platform, with wide-ranging applications in urban and environmental studies.
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