Seismic attribute analysis is useful in identifying faults and quickly visualizing their 3-D geometrical features.This study presents seismic attribute analysis of 3-D seismic reflection data acquired from the Jeju Ba...Seismic attribute analysis is useful in identifying faults and quickly visualizing their 3-D geometrical features.This study presents seismic attribute analysis of 3-D seismic reflection data acquired from the Jeju Basin,offshore southern Korea for fault interpretation.To improve the accuracy of the fault interpretation,noise reduction and spectral enhancement were conducted prior to the seismic attribute analysis.The pre-conditioned seismic volume was used to generate three seismic attribute volumes:dip,tensor,and structurally oriented semblance.These three attribute volumes were co-rendered to compare three different structural attributes effectively.Frequency decomposition volume was created on the basis of the scale,so geological features can be distinctively resolved.Based on the multi-attribute analysis,we quickly identified structural features and accurately interpreted the fault patterns based on their strike direction.Rift-related major faults were identified,and their geometrical characteristics were easily extracted from the 3-D attribute volume.Subtle changes in the strike direction of some normal faults were observed during the rift basin evolution.This observation suggests that the fault development pattern was possibly affected by the change in the magnitude and direction of the extensional stress during the basin evolution or the interaction of the preexisting structural trend and tectonic movement.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
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supported by the Basic Research Program(GP2020-006)of the Korea Institute of Geoscience and Mineral Resources(KIGAM)by the Korea National Oil Corporation(KNOC)Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(2021R1I1A1A01052612).
文摘Seismic attribute analysis is useful in identifying faults and quickly visualizing their 3-D geometrical features.This study presents seismic attribute analysis of 3-D seismic reflection data acquired from the Jeju Basin,offshore southern Korea for fault interpretation.To improve the accuracy of the fault interpretation,noise reduction and spectral enhancement were conducted prior to the seismic attribute analysis.The pre-conditioned seismic volume was used to generate three seismic attribute volumes:dip,tensor,and structurally oriented semblance.These three attribute volumes were co-rendered to compare three different structural attributes effectively.Frequency decomposition volume was created on the basis of the scale,so geological features can be distinctively resolved.Based on the multi-attribute analysis,we quickly identified structural features and accurately interpreted the fault patterns based on their strike direction.Rift-related major faults were identified,and their geometrical characteristics were easily extracted from the 3-D attribute volume.Subtle changes in the strike direction of some normal faults were observed during the rift basin evolution.This observation suggests that the fault development pattern was possibly affected by the change in the magnitude and direction of the extensional stress during the basin evolution or the interaction of the preexisting structural trend and tectonic movement.
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.
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.
