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Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier

Asjad Amin, Mohamed Deriche, Muhammad Amir Shafiq, Zhen Wang and Ghassan Al-Regib
Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier
Interpretation (Tulsa) (May 2017) 5 (3): SJ61-SJ79

Abstract

We have developed a dictionary-based classification approach for salt-dome detection within migrated seismic volumes. The proposed workflow uses seismic attributes derived from the gray-level co-occurrence matrix, Gabor filter, and higher order singular-value decomposition to effectively learn and detect the salt bodies. We use an information theoretic framework to rank the seismic attributes as per their salt-dome classification performance. Based on this ranking, we select the top K attributes for dictionary training, testing, and classification. To improve the accuracy of the detected salt bodies and make the proposed workflow robust to different data sets, we introduce a refining step that uses edge strength and energy values to detect the shape of the salt-dome boundary within the classified patches. The optimal set of attributes and the refining step ensure that the proposed workflow yields good results for detecting salt-dome boundaries even in the presence of weak seismic reflections. We use the seismic data from the Netherlands offshore F3 block (North Sea) to demonstrate the effectiveness of the proposed workflow in detecting salt bodies. Using subjective and objective evaluation metrics, we compare the results of the proposed workflow with existing gradient-, texture-, and patch-based classification methods. The experimental results show that the proposed workflow outperforms existing salt-dome delineation techniques in terms of accuracy and precision.


ISSN: 2324-8858
EISSN: 2324-8866
Serial Title: Interpretation (Tulsa)
Serial Volume: 5
Serial Issue: 3
Title: Automated salt-dome detection using an attribute ranking framework with a dictionary-based classifier
Affiliation: King Fahd University of Petroleum and Minerals, Center for Energy and Geo Processing, Dhahran, Saudi Arabia
Pages: SJ61-SJ79
Published: 201705
Text Language: English
Publisher: Society of Exploration Geophysicists, Tulsa, OK, United States
References: 29
Accession Number: 2017-085793
Categories: Structural geologyApplied geophysics
Document Type: Serial
Bibliographic Level: Analytic
Illustration Description: illus. incl. 5 tables, sects.
Secondary Affiliation: Georgia Institute of Technology, USA, United States
Country of Publication: United States
Secondary Affiliation: GeoRef, Copyright 2017, American Geosciences Institute. Reference includes data from GeoScienceWorld, Alexandria, VA, United States. Reference includes data supplied by Society of Exploration Geophysicists, Tulsa, OK, United States
Update Code: 201745
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