<?xml version="1.1" encoding="utf-8"?>
<article xsi:noNamespaceSchemaLocation="http://jats.nlm.nih.gov/publishing/1.1/xsd/JATS-journalpublishing1-mathml3.xsd" dtd-version="1.1" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance"><front><journal-meta><journal-id journal-id-type="publisher-id">JSE</journal-id><journal-title-group><journal-title>Journal of Seismic Exploration</journal-title></journal-title-group><issn>0963-0651</issn><eissn/><publisher><publisher-name>AccScience Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi"/><article-categories><subj-group subj-group-type="heading"><subject>Article</subject></subj-group></article-categories><title>Reducing the pressure on data acquisition and processing: II – Data-driven compression using conic coding</title><url>https://geophysical-press.com/journal/JSE/articles/411</url><author>T. IKELLELUC,STURZUIOAN</author><pub-date pub-type="publication-year"><year>2009</year></pub-date><volume>18</volume><issue>2</issue><history><date date-type="pub"><published-time>2009-04-01</published-time></date></history><abstract>Ikelle, L.T. and Sturzu, I., 2009. Reducing the pressure on data acquisition and processing: II - Data-driven compression using conic coding. Journal of Seismic Exploration, 18: 119-133. We here propose a compression method that we characterize as data-driven compression because it is based on transforms which are data-dependent instead of generic mathematical transforms like wavelet transforms. Furthermore, our data-driven compression can be used in cascaded form with existing arithmetic data-compression methods (that is, a data-driven compression followed by an arithmetic data-compression method) because our data-compression method is based on accuracy, whereas arithmetic data-compression methods are based on precision. Such a cascade application of compression techniques can increase the compression ratio to 100:1 or more.</abstract><keywords>data compression, matrix factorization, data acquisition, data processing, multiplicative update rules</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Bosman, C. and Reiter, E., 1993. Seismic data compression using wavelet transforms. ExpandedAbstr., 63th Ann. Internat. SEG Mtg., Washington D.C.: 1261-1264.Donoho, P.L., Eagrs, R.A. and Villasenor, J.D., 1995. High performance seismic tracecompression. Expanded Abstr., 65th Ann. Internat. SEG Mtg., Houston: 160-163.Ikelle, L.T., 2009. Coding and Decoding: Seismic Data. Elsevier Science Publishers, Amsterdam(in press).Lee, D.D. and Seung, H.S., 1999. Learning the parts of objects by non-negative matrixfactorization. Nature, 401: 788-791.Luo, Y. and Schuster, G.T., 1992. Wavelet packet transform and data compression. ExpandedAbstr., 62th Ann. Internat. SEG Mtg., New Orleans: 1187-1190.Spanias, A.S., Jonsson, S. and Stearns, S.D., 1991. Transform methods for seismic datacompression. IEEE Transact. Geosci. Remote Sens., 29: 407-416.Tage, R., Ramstad, T.A. and Amundsen, L., 2004. Optimization of sub-band coding method forseismic data compression. Geophys. Prosp., 52: 359-378.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
