<?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>Predictive deconvolution from the point of view of kriging</title><url>https://geophysical-press.com/journal/JSE/articles/406</url><author>K. BROADHEADMICHAEL</author><pub-date pub-type="publication-year"><year>2009</year></pub-date><volume>18</volume><issue>3</issue><history><date date-type="pub"><published-time>2009-07-01</published-time></date></history><abstract>Broadhead, M.K., 2009. Predictive deconvolution from the point of view of kriging. Journal of Seismic Exploration, 18: 239-247. We review the geostatistical method of simple kriging and consider its application to the predictive deconvolution problem in seismology. We find that when kriging is applied to the 1D time-series prediction problem, it can be used to obtain the usual Wiener-Levinson system of equations that are normally arrived at with the methods of stationary time-series analysis. The kriging weights can then be interpreted in terms of the prediction filter coefficients. This connection between predictive decon and geostatistics does not appear to be known. Perhaps a better understanding and exploitation of this type of connection can lead to productive synergies between signal processing and geostatistics.</abstract><keywords>deconvolution, geostatistics, kriging, prediction filter, Wiener-Levinson, covariance</keywords></article-meta></front><body/><back><ref-list><ref id="B1" content-type="article"><label>1</label><element-citation publication-type="journal"><p>Dubrule, O., 2003. Geostatistics for Seismic Data Integration in Earth Models. SEG, Tulsa, OK.Goovaerts, P., 1997. Geostatistics for Natural Resources Evaluation. Oxford Univ. Press, Oxford.Hansen, T.M., Journel, A.G., Tarantola, A. and Mosegaard, K., 2006. Linear inverse Gaussiantheory and geostatistics. Geophysics, 71: R101-R111.Krige, D.G., 1951. A statistical approach to some basic mine valuation problems on theWitwatersrand. J. of the Chem., Metal. and Mining Soc. of South Africa, 52: 119-139.Matheron, G., 1963. Principles of geostatistics. Economic Geol., 58: 1264-1266.Piazza, J.L., Sandjivy, L. and Legeron, S., 1997. Use of geostatistics to improve seismic velocities:Case studies. Expanded Abstr., 67th Ann. Internat. SEG Mtg. Dallas: 1293-1296.Robinson, E.A., 1976. Physical Applications of Stationary Time-Series: with special reference todigital data processing of seismic signals. Macmillan Inc., New York.Robinson, E.A. and Treitel, S., 2000. Geophysical Signal Analysis. SEG, Tulsa, OK.Ruiz-Alzola, J., Alberola-Lopez, C. and Westin, C.F., 2005. Kriging filters for multidimensionalsignal processing. Signal Proces., 85: 413-439.</p><pub-id pub-id-type="doi"/></element-citation></ref></ref-list></back></article>
