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FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY

Abstract: 

In this paper, we present an approach for detecting faults within seismic volumes using a saliency detection framework that employs a 3D-FFT local spectra and multi-dimensional plane projections. The projection scheme divides a 3D-FFT local spectrum into three distinct components, each depicting variations along different dimensions of the data. To detect seismic structures oriented at different angles and to capture directional features within 3D volume, we modify the center-surround model to incorporate directional comparisons around each voxel. The weighted combination of the obtained features then yields a saliency map. Experimental results on a real seismic dataset from the Great South Basin in New Zealand show the effectiveness of the proposed algorithm in the detection of complex fault networks, which are hardly conspicuous within original seismic volume. The subjective evaluation of the results show that the proposed method outperforms the state-of-the-art saliency algorithms and seismic attributes in detecting complex structures and holds a promising future in computer-aided extraction of other geologic features as well.

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Paper Details

Authors:
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche
Submitted On:
13 April 2018 - 10:21pm
Short Link:
Type:
Poster
Event:
Presenter's Name:
Mohammed Deriche
Document Year:
2018
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Document Files

ICASSP2018_SeisSal_Poster.pdf

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[1] Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche, "FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY ", IEEE SigPort, 2018. [Online]. Available: http://sigport.org/2784. Accessed: Dec. 12, 2018.
@article{2784-18,
url = {http://sigport.org/2784},
author = {Muhammad Amir Shafiq; Zhiling Long; Haibin Di; Ghassan AlRegib; and Mohammed Deriche },
publisher = {IEEE SigPort},
title = {FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY },
year = {2018} }
TY - EJOUR
T1 - FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY
AU - Muhammad Amir Shafiq; Zhiling Long; Haibin Di; Ghassan AlRegib; and Mohammed Deriche
PY - 2018
PB - IEEE SigPort
UR - http://sigport.org/2784
ER -
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. (2018). FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY . IEEE SigPort. http://sigport.org/2784
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche, 2018. FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY . Available at: http://sigport.org/2784.
Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. (2018). "FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY ." Web.
1. Muhammad Amir Shafiq, Zhiling Long, Haibin Di, Ghassan AlRegib, and Mohammed Deriche. FAULT DETECTION USING ATTENTION MODELS BASED ON VISUAL SALIENCY [Internet]. IEEE SigPort; 2018. Available from : http://sigport.org/2784