Reverse time migration imaging of tunnels via the finite element method using an unstructured mesh

Wavefield extrapolation is critical in reverse time migration(RTM).The finite diff erence method is primarily used to achieve wavefi eld extrapolation in case of the RTM imaging of tunnels.However,complex tunnel models,including those for karsts and fault fracture zones,are constructed using regular grids with straight curves,which can cause numerical dispersion and reduce the imaging accuracy.In this study,wavefi eld extrapolation was conducted for tunnel RTM using the finite element method,wherein an unstructured mesh was considered to be the body-fi tted partition in a complex model.Further,a Poynting vector calculation equation suitable for the unstructured mesh considered in the fi nite element method was established to suppress the interference owing to low-frequency noise.The tunnel space was considered during wavefi eld extrapolation to suppress the mirror artifacts based on the fl exibility of mesh generation.Finally,the infl uence of the survey layouts(one and two sidewalls)on the tunnel imaging results was investigated.The RTM results obtained for a simple tunnel model with an inclined interface demonstrate that the method based on unstructured meshes can effectively suppress the low-frequency noise and mirror artifacts,obtaining clear imaging results.Furthermore,the two-sidewall tunnel survey layout can be used to accurately obtain the real position of the inclined interface ahead of the tunnel face.The complex tunnel numerical modeling and actual data migration results denote the eff ectiveness of the fi nite element method in which an unstructured mesh is used.

Realtime prediction of hard rock TBM advance rate using temporal convolutional network(TCN)with tunnel construction big data

Real-time dynamic adjustment of the tunnel bore machine(TBM)advance rate according to the rockmachine interaction parameters is of great significance to the adaptability of TBM and its efficiency in construction.This paper proposes a real-time predictive model of TBM advance rate using the temporal convolutional network(TCN),based on TBM construction big data.The prediction model was built using an experimental database,containing 235 data sets,established from the construction data from the Jilin Water-Diversion Tunnel Project in China.The TBM operating parameters,including total thrust,cutterhead rotation,cutterhead torque and penetration rate,are selected as the input parameters of the model.The TCN model is found outperforming the recurrent neural network(RNN)and long short-term memory(LSTM)model in predicting the TBM advance rate with much smaller values of mean absolute percentage error than the latter two.The penetration rate and cutterhead torque of the current moment have significant influence on the TBM advance rate of the next moment.On the contrary,the influence of the cutterhead rotation and total thrust is moderate.The work provides a new concept of real-time prediction of the TBM performance for highly efficient tunnel construction.