Unknown geology ahead of the tunnel boring machine(TBM)brings a large safety risk for tunnel construction.Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection,meeting the...Unknown geology ahead of the tunnel boring machine(TBM)brings a large safety risk for tunnel construction.Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection,meeting the requirements of TBM rapid drilling.Seismic wavefield retrieval is the key data processing step for the efficient utilization of TBM drilling noise.The traditional solution is based on cross-correlation to extract reflected waves,but the reference waves remain in the result,disturbing the imaging and interpre-tation of the adverse geology.To solve this problem,the deep learning method was introduced in wavefield retrieval to improve the accu-racy of geological prospecting.We trained a deep neural network(DNN)with its strong nonlinear mapping capability to transform seismic data from TBM drilling noise to data from the active source.The issue lies in its features for this specific tunnel task,including the decay of the seismic signal with time and the incomplete spatial correspondence.Thus,we improved a classical DNN with the time constraint as an additional input,and an additional pre-decoder to enlarge the receptive field.Additionally,a loss function weighted by the ground truth and time constraint is improved to achieve an accurate retrieval of the effective signal,considering the little effective information in tunnel data.Finally,the workflow of the proposed method was given,and a dataset designed with reference to the field case was employed to train the network.The proposed method accurately retrieved the reflection signal with higher dominant frequen-cies,which helped improve the accuracy of imaging.Numerical simulations and imaging on typical geological models show that the pro-posed method can suppress reference waves and get more accurate results with fewer artifacts.The proposed method has been applied in the Gaoligongshan Tunnel and imaged two abnormal zones,providing meaningful geological information for TBM drilling and tunnel construction.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.42107165)Young Elite Scientist Sponsorship Program by Cast of China Association for Science and Technology(Grant No.YESS20210144)+1 种基金Shandong Provincial Natural Science Foundation of China(Grant No.ZR2021QE242)Science&Technology Program of Department of Transport of Shandong Province,China(Grant No.2019B47_2).
文摘Unknown geology ahead of the tunnel boring machine(TBM)brings a large safety risk for tunnel construction.Seismic ahead-prospecting using TBM drilling noise as a source can achieve near-real-time detection,meeting the requirements of TBM rapid drilling.Seismic wavefield retrieval is the key data processing step for the efficient utilization of TBM drilling noise.The traditional solution is based on cross-correlation to extract reflected waves,but the reference waves remain in the result,disturbing the imaging and interpre-tation of the adverse geology.To solve this problem,the deep learning method was introduced in wavefield retrieval to improve the accu-racy of geological prospecting.We trained a deep neural network(DNN)with its strong nonlinear mapping capability to transform seismic data from TBM drilling noise to data from the active source.The issue lies in its features for this specific tunnel task,including the decay of the seismic signal with time and the incomplete spatial correspondence.Thus,we improved a classical DNN with the time constraint as an additional input,and an additional pre-decoder to enlarge the receptive field.Additionally,a loss function weighted by the ground truth and time constraint is improved to achieve an accurate retrieval of the effective signal,considering the little effective information in tunnel data.Finally,the workflow of the proposed method was given,and a dataset designed with reference to the field case was employed to train the network.The proposed method accurately retrieved the reflection signal with higher dominant frequen-cies,which helped improve the accuracy of imaging.Numerical simulations and imaging on typical geological models show that the pro-posed method can suppress reference waves and get more accurate results with fewer artifacts.The proposed method has been applied in the Gaoligongshan Tunnel and imaged two abnormal zones,providing meaningful geological information for TBM drilling and tunnel construction.
