Forward prediction for tunnel geology and classification of surrounding rock based on seismic wave velocity layered tomography

Excavation under complex geological conditions requires effective and accurate geological forward-prospecting to detect the unfavorable geological structure and estimate the classification of surround-ing rock in front of the tunnel face.In this work,a forward-prediction method for tunnel geology and classification of surrounding rock is developed based on seismic wave velocity layered tomography.In particular,for the problem of strong multi-solution of wave velocity inversion caused by few ray paths in the narrow space of the tunnel,a layered inversion based on regularization is proposed.By reducing the inversion area of each iteration step and applying straight-line interface assumption,the convergence and accuracy of wave velocity inversion are effectively improved.Furthermore,a surrounding rock classification network based on autoencoder is constructed.The mapping relationship between wave velocity and classification of surrounding rock is established with density,Poisson’s ratio and elastic modulus as links.Two numerical examples with geological conditions similar to that in the field tunnel and a field case study in an urban subway tunnel verify the potential of the proposed method for practical application.

Monitoring of velocity changes based on seismic ambient noise: A brief review and perspective

Over the past two decades,the development of the ambient noise cross-correlation technology has spawned the exploration of underground structures.In addition,ambient noise-based monitoring has emerged because of the feasibility of reconstructing the continuous Green’s functions.Investigating the physical properties of a subsurface medium by tracking changes in seismic wave velocity that do not depend on the occurrence of earthquakes or the continuity of artificial sources dramatically increases the possibility of researching the evolution of crustal deformation.In this article,we outline some state-of-the-art techniques for noise-based monitoring,including moving-window cross-spectral analysis,the stretching method,dynamic time wrapping,wavelet cross-spectrum analysis,and a combination of these measurement methods,with either a Bayesian least-squares inversion or the Bayesian Markov chain Monte Carlo method.We briefly state the principles underlying the different methods and their pros and cons.By elaborating on some typical noisebased monitoring applications,we show how this technique can be widely applied in different scenarios and adapted to multiples scales.We list classical applications,such as following earthquake-related co-and postseismic velocity changes,forecasting volcanic eruptions,and tracking external environmental forcing-generated transient changes.By monitoring cases having different targets at different scales,we point out the applicability of this technology for disaster prediction and early warning of small-scale reservoirs,landslides,and so forth.Finally,we conclude with some possible developments of noise-based monitoring at present and summarize some prospective research directions.To improve the temporal and spatial resolution of passive-source noise monitoring,we propose integrating different methods and seismic sources.Further interdisciplinary collaboration is indispensable for comprehensively interpreting the observed changes.