论文标题
通过深度学习，在斯坦福大学校园内通过电信导管中的光纤电缆检测当地地震
Detecting local earthquakes via fiber-optic cables in telecommunication conduits under Stanford University campus using deep learning
论文作者
论文摘要
随着光纤地震采集的发展，连续密集的地震监测变得越来越容易获得。在电信导管中重新利用纤维电缆，即使在传统的地震米不容易安装的地方，例如在城市地区，也可以以低成本进行地震研究。但是，由于连续流数据的大量，除非我们显着自动化处理工作流程，否则以这种方式收集的数据将浪费。我们使用纤维电缆在斯坦福大学校园内的电信导管中获得的三年收购的数据来培训卷积神经网络（CNN）进行地震检测。我们证明，光纤系统可以有效地补充稀疏的地震计网络以检测局部地震。尽管信噪比较低，但CNN允许可靠的地震检测，甚至检测到先前未触及的事件的小振幅。
With fiber-optic seismic acquisition development, continuous dense seismic monitoring is becoming increasingly more accessible. Repurposing fiber cables in telecommunication conduits makes it possible to run seismic studies at low cost, even in locations where traditional seismometers are not easily installed, such as in urban areas. However, due to the large volume of continuous streaming data, data collected in such a manner will go to waste unless we significantly automate the processing workflow. We train a convolutional neural network (CNN) for earthquake detection using data acquired over three years by fiber cables in telecommunication conduits under Stanford University campus. We demonstrate that fiber-optic systems can effectively complement sparse seismometer networks to detect local earthquakes. The CNN allows for reliable earthquake detection despite a low signal-to-noise ratio and even detects small-amplitude previously-uncataloged events.
