Moving loads such as cars and trains are very useful sources of seismic waves, which can be analyzed to retrieve information on the seismic velocity of subsurface materials using the techniques of ambient noise seismology. This information is valuable for a variety of applications such as geotechnical characterization of the near-surface, seismic hazard evaluation, and groundwater monitoring. However, for such processes to converge quickly, data segments with appropriate noise energy should be selected. Distributed Acoustic Sensing (DAS) is a novel sensing technique that enables acquisition of these data at very high spatial and temporal resolution for tens of kilometers. One major challenge when utilizing the DAS technology is the large volume of data that is produced, thereby presenting a significant Big Data challenge to find regions of useful energy. In this work, we present a highly scalable and efficient approach to process real, complex DAS data by integrating physics knowledge acquired during a data exploration phase followed by deep supervised learning to identify "useful" coherent surface waves generated by anthropogenic activity, a class of seismic waves that is abundant on these recordings and is useful for geophysical imaging. Data exploration and training were done on 130~Gigabytes (GB) of DAS measurements. Using parallel computing, we were able to do inference on an additional 170~GB of data (or the equivalent of 10 days' worth of recordings) in less than 30 minutes. Our method provides interpretable patterns describing the interaction of ground-based human activities with the buried sensors.
翻译:汽车和火车等移动载荷是地震波非常有用的来源,可以加以分析,利用环境噪音地震地震学技术检索关于地下材料地震速度的信息。这种信息对近地表面、地震危险评估和地下水监测等各种应用具有宝贵价值,但是,为了使这一过程迅速汇合,应选择具有适当噪音能量的数据区块。分布式声波遥感是一种新颖的遥感技术,能够以非常高的空间和时间分辨率获取这些数据,数十公里。在使用DAS技术时,一个重大挑战是生成的大量数据,从而对寻找有用能源区域提出了巨大的数据挑战。在这项工作中,我们提出了一个高度可扩缩和高效的方法,通过整合在数据勘探阶段获得的物理知识来处理真实、复杂的DAS数据,随后进行深入监督学习,以查明人为活动产生的“有用”一致的地表波,一种可获取的地震波级,可用于地球物理成像。在130-Gigabytes上进行了数据勘探和培训,用相当于DAS10个地面测算的地面测算法的比我们10个地面测算法的10个地面测算法,我们用10个地面测算法的10个地面测算法提供了比温度的平行测算法。我们用10天的模拟测算方法提供了10天的平行测算。