Multi-scale Bayesian Based Horizon Matchings Across Faults in 3d Seismic Data

  • Fitsum Admasu
  • Klaus Tönnies
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4174)


Oil and gas exploration decisions are made based on inferences obtained from seismic data interpretation. While 3-d seismic data become widespread and the data-sets get larger, the demand for automation to speed up the seismic interpretation process is increasing as well. Image processing tools such as auto-trackers assist manual interpretation of horizons, seismic events representing boundaries between rock layers. Auto-trackers works to the extent of observed data continuity; they fail to track horizons in areas of discontinuities such as faults.

In this paper, we present a method for automatic horizon matching across faults based on a Bayesian approach. A stochastic matching model which integrates 3-d spatial information of seismic data and prior geological knowledge is introduced. A multi-resolution simulated annealing with reversible jump Markov Chain Monte Carlo algorithm is employed to sample from a-posteriori distribution. The multi-resolution is defined in a scale-space like representation using perceptual resolution of the scene. The model was applied to real 3-d seismic data, and has shown to produce horizons matchings which compare well with manually obtained matching references.


Seismic Data Rock Layer Markov Chain Monte Carlo Algorithm Seismic Line Fault Surface 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Fitsum Admasu
    • 1
  • Klaus Tönnies
    • 1
  1. 1.Computer Vision GroupUniversity of MagdeburgMagdeburgGermany

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