To investigate the longitudinal deformation profile(LDP)of a deep tunnel in non-hydrostatic condition,an analytical model is proposed in our study.In this model,the problem is considered as a superposition of two part...To investigate the longitudinal deformation profile(LDP)of a deep tunnel in non-hydrostatic condition,an analytical model is proposed in our study.In this model,the problem is considered as a superposition of two partial models,and the displacement field of the second partial model is the same as that of the concerned problem.Therefore,the problem can be solved by a model with simple boundary conditions.We obtain the solutions for the stress and displacement fields of an infinite body caused by arbitrary surface tractions on the boundary of the coming tunnel(zone inside the tunnel before excavation)by integrating the extended Kelvin solution over the boundary.The obtained stress solution is used to solve the specific surface tractions,which can satisfy the boundary conditions of the second partial model,on the boundary of the coming tunnel in an infinite body.Then,the specific surface tractions are substituted into the obtained displacement solution to solve the displacement on the wall and face of the tunnel.Therefore,the LDP can also be calculated.The proposed solution is verified by both numerical simulation and the LDP functions recommended by other researchers.The major advantage of our analytical model is that it can consider the effects of the axial and horizontal lateral pressure coefficients.It is revealed that the horizontal lateral pressure coefficient majorly affects the LDP behind the tunnel face,while the axial lateral pressure coefficient dominates the LDP ahead of the tunnel face.Furthermore,the deformation characteristics of the LDPs ahead of the face and the unexcavated core are investigated.The axial displacements of the excavation face can be used to predict the crown displacements ahead of the face.展开更多
Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segme...Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segmentation using deep learning.However,few studies employ deep learning in fault instance segmentation.We introduce mask propagation neural network for fault instance segmentation.Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles.Our method refers to the reference-guided mask propagation network,which is firstly used in video object segmentation:taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method.As a multi-level convolutional neural network,the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data,which can facilitate the fault reconstruction workflow.Compared with the traditional deep learning method,the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection.展开更多
基金the financial support by the Key Project of High-speed Rail Joint Fund of National Natural Science Foundation of China(Grant No.U1934210)the Natural Science Foundation of Beijing,China(Grant No.8202037)。
文摘To investigate the longitudinal deformation profile(LDP)of a deep tunnel in non-hydrostatic condition,an analytical model is proposed in our study.In this model,the problem is considered as a superposition of two partial models,and the displacement field of the second partial model is the same as that of the concerned problem.Therefore,the problem can be solved by a model with simple boundary conditions.We obtain the solutions for the stress and displacement fields of an infinite body caused by arbitrary surface tractions on the boundary of the coming tunnel(zone inside the tunnel before excavation)by integrating the extended Kelvin solution over the boundary.The obtained stress solution is used to solve the specific surface tractions,which can satisfy the boundary conditions of the second partial model,on the boundary of the coming tunnel in an infinite body.Then,the specific surface tractions are substituted into the obtained displacement solution to solve the displacement on the wall and face of the tunnel.Therefore,the LDP can also be calculated.The proposed solution is verified by both numerical simulation and the LDP functions recommended by other researchers.The major advantage of our analytical model is that it can consider the effects of the axial and horizontal lateral pressure coefficients.It is revealed that the horizontal lateral pressure coefficient majorly affects the LDP behind the tunnel face,while the axial lateral pressure coefficient dominates the LDP ahead of the tunnel face.Furthermore,the deformation characteristics of the LDPs ahead of the face and the unexcavated core are investigated.The axial displacements of the excavation face can be used to predict the crown displacements ahead of the face.
基金Supported by Natural Science Foundation of China(U1562218 and 41974147)the authors would like to thank X.M.Wu for his public seismic synthetic data set.
文摘Fault interpretation plays a critical role in understanding the crustal development and exploring the subsurface reservoirs such as gas and oil.Recently,significant advances have been made towards fault semantic segmentation using deep learning.However,few studies employ deep learning in fault instance segmentation.We introduce mask propagation neural network for fault instance segmentation.Our study focuses on the description of the differences and relationships between each fault profile and the consistency of fault instance segmentations with adjacent profiles.Our method refers to the reference-guided mask propagation network,which is firstly used in video object segmentation:taking the seismic profiles as video frames while the seismic data volume as a video sequence along the inline direction we can achieve fault instance segmentation based on the mask propagation method.As a multi-level convolutional neural network,the mask propagation network receives a small number of user-defined tags as the guidance and outputs the fault instance segmentation on 3D seismic data,which can facilitate the fault reconstruction workflow.Compared with the traditional deep learning method,the introduced mask propagation neural network can complete the fault instance segmentation work under the premise of ensuring the accuracy of fault detection.
