Description

• Title:
  An attempt to apply artificial neural networks in integrated interpretation of electromagnetic data - the Grabownica hydrocarbon deposit case study
• Creator:
  MASTEJ, Wojciech
• Description:
  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.
• Object availability:
  free access
• Date:
  2011.01.01
• Type:
  info:eu-repo/semantics/article;
  info:eu-repo/semantics/publishedVersion
• Language:
  English
• Source:
  Geology, Geophysics and Environment; Vol. 37 No. 1 (2011); 157;
  2353-0790;
  2299-8004
• Format:
  application/pdf
• Publisher:
  AGH University Press
• Relation: https://journals.agh.edu.pl/geol/article/view/217/143
• Subject:
  pattern recognition;
  artificial neural networks;
  magnetotellurics;
  hydrocarbon deposits
• Identifier: https://journals.agh.edu.pl/geol/article/view/217 ;
  10.7494/geol.2011.37.1.157
• Data provider:
  AGH University of Science and Technology Journals
• Can I use it?:
  seek permission
• Type:
  text