A fast coherence algorithm for seismic data interpretation based on information divergence

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

We propose a new fast coherence method of for robust and efficient estimation of 3-D seismic data in which the measure is characterized by greatly increasing the computation efficiency and stability via information divergence and called information divergence coherence algorithm (IDCA). The major goal of this method is solved existing problem which coherence calculations required for the building of large covariance metric and the computation of their dominant eigenvalue in the third generation coherence estimate based on an eigen-structure approach(C3). To avoid directly computing the eigenvalues of covariance matrix for greatly reducing the time cost of coherence calculation. In contrast to C3, the IDCA image possesses higher resolution and better anti-noise ability. The advantage of this new method is also demonstrated by real seismic data examples.

Original languageEnglish
Title of host publicationSociety of Exploration Geophysicists International Exposition and 83rd Annual Meeting, SEG 2013
Subtitle of host publicationExpanding Geophysical Frontiers
PublisherSociety of Exploration Geophysicists
Pages2554-2558
Number of pages5
ISBN (Print)9781629931883
DOIs
StatePublished - 2019
EventSociety of Exploration Geophysicists International Exposition and 83rd Annual Meeting: Expanding Geophysical Frontiers, SEG 2013 - Houston, United States
Duration: 22 Sep 201327 Sep 2013

Publication series

NameSociety of Exploration Geophysicists International Exposition and 83rd Annual Meeting, SEG 2013: Expanding Geophysical Frontiers

Conference

ConferenceSociety of Exploration Geophysicists International Exposition and 83rd Annual Meeting: Expanding Geophysical Frontiers, SEG 2013
Country/TerritoryUnited States
CityHouston
Period22/09/1327/09/13

Fingerprint

Dive into the research topics of 'A fast coherence algorithm for seismic data interpretation based on information divergence'. Together they form a unique fingerprint.

Cite this