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Inversion of vehicle-induced signals based on seismic interferometry and recurrent neural networks
Geophysics ( IF 3.0 ) Pub Date : 2021-04-08 , DOI: 10.1190/geo2020-0498.1
Lu Liu 1 , Yujin Liu 1 , Tao Li 2 , Yi He 1 , Yue Du 1 , Yi Luo 3
Affiliation  

Vehicle-induced vibrations provide useful signals for passive seismic exploration. Such signals are repeatable and environmentally friendly; hence, they can provide an economical way to analyze subsurface structures. We have developed a new workflow to monitor roads or railways by producing 1D subsurface S-wave velocities in real time. This workflow consists of two steps: seismic interferometry and recurrent neural networks (RNN). Seismic interferometry can efficiently retrieve surface waves by crosscorrelating vehicle-induced vibrations. RNN was designed to first encode the picked dispersion curve into a fixed-length vector and then decode the vector into 1D S-wave velocities. To simulate the railway vibrations, we first analyze the time-dependent characteristic of the high-speed-train source and verify its mathematical expression by comparing the frequency spectrums of the real and synthetic data. We then evaluate the RNN-based surface-wave dispersion inversion method and validate the designed network structure using the 3D SEG/EAGE overthrust model. Finally, seismic interferometry and RNN-based surface-wave inversion are applied to a synthetic train-induced data set, a 33 min field record of railway vibrations and a 76 min field data of road vibrations, respectively. The synthetic and field data tests indicated that our workflow can be a feasible and cost-effective tool for real-time monitoring of subsurface media along roads and railways.

中文翻译:

基于地震干涉法和递归神经网络的车辆感应信号反演

车辆引起的振动为被动地震勘探提供了有用的信号。这种信号是可重复的并且对环境友好;因此,它们可以为分析地下结构提供一种经济的方法。我们已经开发了一种新的工作流程,可以实时产生一维地下S波速度来监控道路或铁路。该工作流程包括两个步骤:地震干涉法和递归神经网络(RNN)。地震干涉法可以使车辆引起的振动相互关联,从而有效地恢复表面波。RNN被设计为首先将拾取的色散曲线编码为固定长度的向量,然后将其解码为一维S波速度。为了模拟铁路振动,我们首先分析高速火车源的时变特性,然后通过比较真实数据和合成数据的频谱来验证其数学表达式。然后,我们评估基于RNN的面波频散反演方法,并使用3D SEG / EAGE上推模型验证设计的网络结构。最后,将地震干涉法和基于RNN的面波反演分别应用于火车的综合数据集,铁路振动的33分钟现场记录和道路振动的76分钟现场数据。综合和现场数据测试表明,我们的工作流程可以成为一种可行且具有成本效益的工具,用于实时监控公路和铁路的地下介质。然后,我们评估基于RNN的面波频散反演方法,并使用3D SEG / EAGE上推模型验证设计的网络结构。最后,将地震干涉法和基于RNN的面波反演分别应用于火车的综合数据集,铁路振动的33分钟现场记录和道路振动的76分钟现场数据。综合和现场数据测试表明,我们的工作流程可以成为一种可行且具有成本效益的工具,用于实时监控公路和铁路的地下介质。然后,我们评估基于RNN的面波频散反演方法,并使用3D SEG / EAGE上推模型验证设计的网络结构。最后,将地震干涉法和基于RNN的面波反演分别应用于火车的综合数据集,铁路振动的33分钟现场记录和道路振动的76分钟现场数据。综合和现场数据测试表明,我们的工作流程可以成为一种可行且具有成本效益的工具,用于实时监控公路和铁路的地下介质。分别。综合和现场数据测试表明,我们的工作流程可以成为一种可行且具有成本效益的工具,用于实时监控公路和铁路的地下介质。分别。综合和现场数据测试表明,我们的工作流程可以成为一种可行且具有成本效益的工具,用于实时监控公路和铁路的地下介质。
更新日期:2021-04-09
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