Opis

• Tytuł:
  An attempt to apply artificial neural networks in integrated interpretation of electromagnetic data - the Grabownica hydrocarbon deposit case study
• Twórca:
  MASTEJ, Wojciech
• Opis:
  The paper presents an attempt to apply the pattern recognition method using artificial neural networks in integrated interpretation of the magnetotelluric data AMT (Audio-frequency Magnetotellurics) and CSAMT (Controlled Source Audio-frequency Magnetotellurics), acquired from the Grabownica oil and gas deposit (longitudinal and transversal dipole in relation to measurement profile lines). Vertical belts of magnetotelluric cross-sections, each 50 m wide, obtained from interpolation grids, were the objects of classification. The structure of formally non-structural objects was partly imposed through division into: near-surface high-resistivity zone, to 200 m a.s.l., low-resistivity screens and week maxima - diffusion chimneys, from 200 to -100 m a.s.l., and the lowest, high-resistivity deposit zone. The usefulness of the method was proven by correct identification of real deposit zones based on data often coming from ambiguous and incompatible magnetotelluric cross-sections. In addition, two unknown deposit zones were discovered in NE part of cross-section G4 and SW part of cross-section G2.
• Dostępność obiektu:
  Dostęp otwarty
• Data:
  2011.01.01
• Typ:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Język:
  angielski
• Źródło:
  2353-0790;
  2299-8004
• Format:
  application/pdf
• Wydawca:
  AGH University Press
• Relacja: https://journals.agh.edu.pl/geol/article/view/217/143
• Temat i słowa kluczowe:
  pattern recognition;
  artificial neural networks;
  magnetotellurics;
  hydrocarbon deposits
• Identyfikator: https://journals.agh.edu.pl/geol/article/view/217 ;
  10.7494/geol.2011.37.1.157
• Dostawca danych:
  AGH University of Science and Technology Journals
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