Bayesian 3-D Path Search and Its Applications to Focusing Seismic Data

  • R. Azencott
  • B. Chalmond
  • Ph. Julien
Part of the Lecture Notes in Statistics book series (LNS, volume 74)


The 3D-images studied here are essential to the analysis of cubes of seismic focalisation. In the detection of geological horizons, the improvement of migration techniques requires the construction of 3D “focal” paths. We start with blurred versions of (unknown) 3D-images consisting ideally of concentrated intensity spots which tend to lie on smooth isolated 3D-paths. The blur point-spread function is spatially dependent, roughly Gaussian in shape, and directly estimated on the blurred image. On the space of admissible paths, we describe the plausibility of a path by an energy function, using thus a 3D-Markov random field model. The adjustment of this Markov field model to the image data relies on an original interactive robust parameter localization approach.

Reconstruction of the original paths is based on a maximum (a posteriori) likelihood approach, implemented by a new variant of Besag’s ICM algorithm. Applications to actual 3D-seismic data are presented.


Seismic Data Pointer Field Depth Migration Blur Kernel Seismic Trace 
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 1992

Authors and Affiliations

  • R. Azencott
    • 1
  • B. Chalmond
    • 2
  • Ph. Julien
    • 3
  1. 1.École Normale Supérieure (Paris) et Université Paris-SudFrance
  2. 2.Université Paris-SudFrance
  3. 3.TOTAL-CFP, Geophysical ResearchUSA

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