Abstract
Seismic attribute analysis is the most effective method to predict geological features from seismic images. However, using a single attribute for the purpose may reduce the prediction quality. Therefore, integrating multiple attributes becomes significant and has the potential to decipher the finer details. The present study uses the principal component analysis to carry out a multi-attribute study for long offset (5 km) seismic data from the Krishna-Godavari basin, Eastern Margin of India, so as to improve the seismic image and aid in feature extraction. The colour composites developed for the seismic data indicate improved continuity in reflectors in stratigraphic attributes analysis and better resolution of the faults and migration pathways in structural attributes analysis.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Data availability
Data will be available on request to the corresponding author.
References
Ali KL, Reza RM, Hossain RB, Ali C (2009) Petrophysical data prediction from seismic attributes using committee fuzzy inference system. Comput Geosci 35:2314–2330. https://doi.org/10.1016/j.cageo.2009.04.010
Arts RJ, Ampilov YP (1999) Enhancement of a flat spot from seismic data using multi-attribute analysis. 61st EAGE Conference and Exhibition, European Association of Geoscientists & Engineers cp132–00071. https://doi.org/10.3997/2214-4609.201407668
Chenin J, Bedle H (2020) Multi-attribute machine learning analysis for weak BSR detection in the Pegasus Basin. Offshore New Zealand Marine Geophysical Research 41:21. https://doi.org/10.1007/s11001-020-09421-x
Kumar J, Sain K, Arun KP (2018) Seismic attributes for characterizing gas hydrates: a study from the Mahanadi offshore, India. Marine Geophysical Research 40:73–86. https://doi.org/10.1007/s11001-018-9357-4
Lin I, Marfurt KJ, Johnson O (2003) Mapping 3D multiattribute data into HLS color space -applications to Vinton dome, Louisiana. 73rd Annual International Meeting, SEG Expanded Abstracts 1728–1731. https://doi.org/10.1190/1.1817642
Liner C (1999) Elements of 3-D seismology. PennWell, Tulsa, OK. Ramprasad T, Dewangan P, Ramana MV, Mazumdar A, Karisiddaiah SM, Ramya ER, Sriram G (2011) Evidence of slumping/sliding in Krishna-Godavari offshore basin due to gas/fluid movements. Mar Pet Geol 28:1806–1816. https://doi.org/10.1016/j.marpetgeo.2011.02.007
Ramu R, Sain K (2021) Multi-attribute and artificial neural network analysis of seismic inferred chimney-like features in marine sediments: a study from KG Basin, India. J Geol Soc India 97:238–242. https://doi.org/10.1007/s12594-021-1672-8
Roden R, Chen CW (2017) Interpretation of DHI characteristics with machine learning. First Break 35(5):55–63. https://doi.org/10.3997/1365-2397.35.5.88069
Roden R, Smith T, Sacrey D (2015) Geologic pattern recognition from seismic attributes: principal component analysis and self-organizing maps. Interpretation 3(4):SAE59–SAE83. https://doi.org/10.1190/INT-2015-0037.1
Satyavani N, Sain K (2015) Seismic insights into bottom simulating reflection (BSR) in the Krishna-Godavari Basin, Eastern Margin of India. Mar Georesour Geotechnol 33(3):191–201. https://doi.org/10.1080/1064119X.2013.797059
Singh Y (2007) Lithofacies detection through simultaneous inversion and principal component attributes. Lead Edge 26(12):1568–1575. https://doi.org/10.1190/1.2821944
Soltani P, Soleimani M, Aghajani H (2017) Faults and fractures detection in D seismic data based on principal component analysis. Int J Mining and Geo-Eng 51:199–207. https://doi.org/10.22059/ijmge.2017.212587.594618
Acknowledgements
The authors wish to thank Director, CSIR-National Geophysical Research Institute, for his kind consent to publish this work (Ref. No: NGRI/Lib/2020/Pub-19). The first author is grateful to the Department of Science and Technology, Government of India, for providing him with the Inspire Fellowship. The Directorate General of Hydrocarbons (DGH), New Delhi, is thanked for providing the seismic data to NGRI. This work is carried out in the Project MLP-FBR-0008 of CSIR-NGRI.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors have no relevant financial or non-financial interests to disclose.
The authors have no conflicts of interest to declare that are relevant to the content of this article.
All authors certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.
The authors have no financial or proprietary interests in any material discussed in this article.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Ramesh, A., Satyavani, N. & Attar, M.R.S. Improved feature extraction in seismic data: multi-attribute study from principal component analysis. Geo-Mar Lett 41, 48 (2021). https://doi.org/10.1007/s00367-021-00719-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s00367-021-00719-2