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research article

Robust and adaptive approaches for Relative Geologic Time Estimation

Arouna, Moctar Mounirou
•
El Gheche, Mireille  
•
Donias, Marc
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December 1, 2018
Journal Of Applied Geophysics

For a geoscientist, the Relative Geologic Time (RGT) is an important tool to perform chronostratigraphic analysis. However, automatically estimate an RGT image from a seismic image can be a challenging task where we have to respect seismic features, the deposit orders and to deal efficiently with unconformities such as erosions, progradating systems, etc. To this end, approaches have been proposed formulating the estimation problem in a regularized convex optimization problem. However none of these fully address efficiently all issues. In this paper, we propose a new regularization term based on an asymmetric and adaptive weight function. The asymmetric behavior focuses on the ill-posed problem while the adaptive process is used to deal with unconformities by modulating the strength of the regularization if necessary. Moreover, to increase the robustness of the approach, we propose variants of the method in terms of l(1)-norm instead of l(2)-norm that corresponds to potentially too smooth solutions. For evaluating the relevance of our proposals, experimentations have been conducted on both synthetic and real seismic images. (C) 2018 Elsevier B.V. All rights reserved.

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Type
research article
DOI
10.1016/j.jappgeo.2018.07.013
Web of Science ID

WOS:000453644600015

Author(s)
Arouna, Moctar Mounirou
El Gheche, Mireille  
Donias, Marc
Guillon, Sebastien
Berthoumieu, Yannick
Date Issued

2018-12-01

Publisher

ELSEVIER SCIENCE BV

Published in
Journal Of Applied Geophysics
Volume

159

Start page

157

End page

172

Subjects

Geosciences, Multidisciplinary

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Mining & Mineral Processing

•

Geology

•

Mining & Mineral Processing

•

relative geologic time (rgt)

•

regularizations

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convex optimization

•

penality functions

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proximal algorithms

•

flattening

Editorial or Peer reviewed

REVIEWED

Written at

EPFL

EPFL units
LTS4  
Available on Infoscience
January 3, 2019
Use this identifier to reference this record
https://infoscience.epfl.ch/handle/20.500.14299/153323
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