Seismic Attributes Similarity in Facies Classification

  • Marcin Lewandowski
  • Łukasz Słonka
Part of the Studies in Big Data book series (SBD, volume 40)


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.


Seismic attributes Geophysics Correlation Similarity Machine learning 



Seismic data from the Kłodawa-Ponetów–Wartkowice area was acquired for the BlueGas-JuraShale project (BG1/JURASHALE/13) funded by the Polish National Centre for Research and Development (NCBiR). San Leon Energy is thanked providing access to seismic data from the Nida Trough. We would also thank to Paradigm \(\circledR \) for providing academic software license for seismic attributes extraction and to the authors and developers of Python, NumPy, Matplotlib and ObsPy.


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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Institute of Computer ScienceWarsaw University of TechnologyWarszawaPoland
  2. 2.Institute of Geological SciencesPolish Academy of SciencesWarsawPoland

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