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
The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in parti...The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aquifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments conducted an elevated permeability zone.展开更多
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.
文摘The region of investigation is part of the western desert of Iraq covering an area of about 12,400 km2, this region includes several large wadis discharging to the Euphrates River. Since the Tectonic features in particular fault zones play a significant role with respect to groundwater flow in hard rock terrains. The present research is focus on investigate lineaments that have been classified as suspected faults by means of remote sensing techniques and digital terrain evaluation in combination with interpolating groundwater heads and MLU pumping tests model in a fractured rock aquifer, Lineaments extraction approach is illustrated a fare matching with suspected faults, moreover these lineaments conducted an elevated permeability zone.
