Abstract
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.
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Notes
- 1.
Attributes revisited http://www.rocksolidimages.com/attributes-revisited.
- 2.
Paradigm - E&P Subsurface Software Solutions, http://www.pdgm.com/.
- 3.
SEG Y rev1 Data Exchange Format, http://www.seg.org/documents/10161/77915/seg_y_rev1.pdf.
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Acknowledgements
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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Lewandowski, M., Słonka, Ł. (2019). Seismic Attributes Similarity in Facies Classification. In: Bembenik, R., Skonieczny, Ł., Protaziuk, G., Kryszkiewicz, M., Rybinski, H. (eds) Intelligent Methods and Big Data in Industrial Applications. Studies in Big Data, vol 40. Springer, Cham. https://doi.org/10.1007/978-3-319-77604-0_13
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