@inproceedings{b13e5a9b0bd94d9a9befe024a0807d72,
title = "Automatic fault tracking across seismic volumes via tracking vectors",
abstract = "The identification of reservoir regions has a close relationship with the detection of faults in seismic volumes. However, only relying on human intervention, most fault detection algorithms are inefficient. In this paper, we present a new technique that automatically tracks faults across a 3D seismic volume. To implement automation, we propose a two-way fault line projection based on estimated tracking vectors. In the tracking process, projected fault lines are integrated into a synthesized line as the tracked fault line, through an optimization process with local geological constraints. The tracking algorithm is evaluated using real-world seismic data sets with promising results. The proposed method provides comparable accuracy to the detection of faults explicitly in every seismic section, and it also reduces computational complexity.",
keywords = "3D seismic interpretation, fault detection and tracking, geological optimization, motion vectors",
author = "Zhen Wang and Zhiling Long and Ghassan Alregib and Amin Asjad and Deriche, \{Mohamed A.\}",
note = "Publisher Copyright: {\textcopyright} 2014 IEEE.",
year = "2014",
month = jan,
day = "28",
doi = "10.1109/ICIP.2014.7026182",
language = "English",
series = "2014 IEEE International Conference on Image Processing, ICIP 2014",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "5851--5855",
booktitle = "2014 IEEE International Conference on Image Processing, ICIP 2014",
address = "United States",
}