Similarity index for seismic data sets using adaptive curvelets

Hasan Al-Marzouqi, Ghassan AlRegib

Research output: Contribution to journalConference articlepeer-review

5 Scopus citations


In this paper, we propose a distance measure to evaluate visual similarity between two images. The algorithm searches for adaptive curvelet parameters that better represent a reference image. Next, the distance between the reference image and other images is computed as a weighted sum of distances between histograms of adaptive curvelet coefficients. The algorithm is tested on a data set of exemplary seismic activities. The developed measure is shown to be effective in extracting correct matches from the data set.

Original languageBritish English
Pages (from-to)1470-1474
Number of pages5
JournalSEG Technical Program Expanded Abstracts
StatePublished - 2014
EventSEG Denver 2014 Annual Meeting, SEG 2014 - Denver, United States
Duration: 26 Oct 201131 Oct 2011


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