Inversion of vehicle-induced signals based on seismic interferometry and recurrent neural networks

Vehicle-induced vibrations provide useful signals for passive seismic exploration. Such signals are repeatable and environmentally friendly, and hence can provide an economical way to analyze subsurface structures. We propose a new workflow to monitor the roads or railways by producing 1-D subsurface shear-wave velocities in real time. This workflow consists of two steps: seismic interferometry and recurrent neural networks (RNN). Seismic interferometry can efficiently retrieve the surface waves by crosscorrelating the vehicle-induced vibrations. The RNN is designed to first encode the picked dispersion curve into a fixed-length vector and then decode the vector into 1-D shear-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 spectrum of real data and the synthetic one. We then introduce 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-minute field record of railway vibrations and a 76-minute field data of road vibrations, respectively. Both of the synthetic and field data tests show that the proposed workflow can be a feasible and cost-effective tool for real-time monitoring of the subsurface media along roads and railways.