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A deep learning method for automatic fault detection

Authors
Yue Ma (Aramco Research Center - Beijing, Aramco Asia) | Xu Ji (EXPEC Advanced Research Center, Saudi Aramco) | Nasher M. BenHassan (EXPEC Advanced Research Center, Saudi Aramco) | Yi Luo (EXPEC Advanced Research Center, Saudi Aramco)
Document ID
SEG-2018-2984932
Publisher
Society of Exploration Geophysicists
Source
2018 SEG International Exposition and Annual Meeting, 14-19 October, Anaheim, California, USA
Publication Date
2018
Document Type
Conference Paper
Language
English
Copyright
2018. Society of Exploration Geophysicists
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4 in the last 30 days
90 since 2007
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Price: USD 21.00

Summary

Detecting faults in 3D seismic images is important in seismic interpretation since the faults reveal abrupt subsurface geology changes. We developed a method using a convolutional neural network (CNN) to generate a fault-probability attribute for highlighting fault zones in seismic amplitude images. The proposed method detects faults directly from seismic amplitude cubes, so that precomputed (e.g., coherence or edge detection) attributes are not required. This new method is implemented in two steps - training and prediction. In the training step, a CNN model is trained with annotated real seismic image cubes, where each point is labeled as fault or non-fault. In the prediction step, the trained network is applied to compute the fault probability at each location in the new image cubes. The method is verified with both synthetic and field datasets. Test results show that CNN-based fault probability outperforms coherence technology.

File Size  615 KBNumber of Pages   5

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PetroWiki was initially created from the seven volume  Petroleum Engineering Handbook (PEH) published by the  Society of Petroleum Engineers (SPE).








The SEG Wiki is a useful collection of information for working geophysicists, educators, and students in the field of geophysics. The initial content has been derived from : Robert E. Sheriff's Encyclopedic Dictionary of Applied Geophysics, fourth edition.

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