Automatic fault localization using the generalized Earth Mover's distance

Localizing fault lines and surfaces in seismic subsurface images is a daunting challenge. Existing state-of-the-art approaches usually involve visual interpretation by an expert, but this is time-consuming, expensive and error-prone. In this paper, we propose some initial steps towards a new algorit...

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Bibliographic Details
Main Authors: Schmidt, Ludwig (Contributor), Hegde, Chinmay (Contributor), Indyk, Piotr (Contributor), Kane, Jonathan (Author), Lu, Ligang (Author), Hohl, Detlef (Author)
Other Authors: Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science (Contributor)
Format: Article
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE), 2018-02-22T18:46:51Z.
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Online Access:Get fulltext
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100 1 0 |a Schmidt, Ludwig  |e author 
100 1 0 |a Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science  |e contributor 
100 1 0 |a Schmidt, Ludwig  |e contributor 
100 1 0 |a Hegde, Chinmay  |e contributor 
100 1 0 |a Indyk, Piotr  |e contributor 
700 1 0 |a Hegde, Chinmay  |e author 
700 1 0 |a Indyk, Piotr  |e author 
700 1 0 |a Kane, Jonathan  |e author 
700 1 0 |a Lu, Ligang  |e author 
700 1 0 |a Hohl, Detlef  |e author 
245 0 0 |a Automatic fault localization using the generalized Earth Mover's distance 
260 |b Institute of Electrical and Electronics Engineers (IEEE),   |c 2018-02-22T18:46:51Z. 
856 |z Get fulltext  |u http://hdl.handle.net/1721.1/113867 
520 |a Localizing fault lines and surfaces in seismic subsurface images is a daunting challenge. Existing state-of-the-art approaches usually involve visual interpretation by an expert, but this is time-consuming, expensive and error-prone. In this paper, we propose some initial steps towards a new algorithmic framework for automatic fault localization. The core of our approach is a deterministic model for 2D images that we call the Constrained Generalized Earth Mover's Distance (CGEMD) model. We propose an algorithm that returns the best approximation in the model for any given input 2D image X; the output of this algorithm is then post-processed to reveal the locations of the faults in the image. We demonstrate the validity of this approach on a number of synthetic and real-world examples. 
546 |a en_US 
655 7 |a Article 
773 |t 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)