Digital superresolution in seismic AVO inversion-Reference-Cited by-同舟云学术

Digital superresolution in seismic AVO inversion

Published:2019-10 Issue:10 Volume:38 Page:791-799
ISSN:1070-485X
Container-title:The Leading Edge
language:en
Short-container-title:The Leading Edge

Author:

Kolbj⊘rnsen Odd¹²,Kjelsrud Evensen Andreas¹,Nilsen Espen Harris¹,Lie Jan Erik¹

Affiliation:

1. Lundin-Norway, Lundin Geolab, Lysaker, Norway..

2. University of Oslo, Department of Mathematics, Oslo, Norway.

Abstract

The sparseness promoted by the total variation norm is utilized to achieve superresolution amplitude-variation-with-offset (AVO) inversion. The total variation norm promotes solutions that have constant values within unspecified regions and thus are well suited for an earth model consisting of layers bounded by faults and erosion surfaces. Algorithmic developments from digital image and video restoration are utilized to solve the geophysical problem. A spatial point spread function is used to model the resulting effect of wave propagation, migration, and processing. The methodology is compared to current alternatives and discussed in the context of AVO inversion. Good results are obtained in a Barents Sea test case.

Publisher

Society of Exploration Geophysicists

Subject

Geology,Geophysics

Link

https://library.seg.org/doi/pdf/10.1190/tle38100791.1

Reference19 articles.

1. Antun, V., F. Renna, C. Poon, B. Adcock, and A. C. Hansen, 2019, On instabilities of deep learning in image reconstruction — Does AI come at a cost? arXiv:1902.05300v1.

2. Towards a Mathematical Theory of Super-resolution

3. Total Variation Image Restoration: Overview and Recent Developments

4. An Augmented Lagrangian Method for Total Variation Video Restoration

5. ROBUST MODELING WITH ERRATIC DATA

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