Seismic Attributes Similarity in Facies Classification

Marcin Lewandowski , Łukasz Słonka


Seismic attributes are one of the component of reflection seismology. Formerly the advances in computer technology have led to an increase in number of seismic attributes and thus better geological interpretation. Nowadays, the overwhelming number and variety of seismic attributes make the interpretation less unequivocal and can lead to slow performance. Using the correlation coefficients, similarities and hierarchical grouping the analysis of seismic attributes was carried out on several real datasets. We try to identify key seismic attributes (also the weak ones) that help the most with machine learning seismic attribute analysis and test the selection with Random Forest algorithm. Obtained quantitative factors help with the overall look at the data. Initial tests have shown some regularities in the correlations between seismic attributes. Some attributes are unique and potentially very helpful with information retrieval while others form non-diverse groups. These encouraging results have the potential for transferring the work to practical geological interpretation.
Author Marcin Lewandowski (FEIT / IN)
Marcin Lewandowski,,
- The Institute of Computer Science
, Łukasz Słonka
Łukasz Słonka,,
Publication size in sheets0.5
Book Bembenik Robert, Skonieczny Łukasz, Protaziuk Grzegorz M., Kryszkiewicz Marzena, Rybiński Henryk (eds.): Intelligent Methods and Big Data in Industrial Applications, Studies in Big Data, vol. 40, 2019, Cham, Springer, ISBN 978-3-319-77603-3, [978-3-319-77604-0], 376 p., DOI:10.1007/978-3-319-77604-0
Keywords in Englishseismic attributes, geophysics, correlation, similarity, machine learning
ProjectDevelopment of new algorithms in the areas of software and computer architecture, artificial intelligence and information systems and computer graphics . Project leader: Arabas Jarosław, , Phone: +48 22 234 7432, start date 01-08-2018, end date 30-09-2019, II/2018/DS/1, Completed
WEiTI Działalność statutowa
Languageen angielski
20170155.pdf 865.99 KB
Score (nominal)20
Score sourcepublisherList
ScoreMinisterial score = 20.0, 02-02-2020, ChapterFromConference
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