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Author: Admin | 2025-04-27
Skip Nav Destination Research Article| January 15, 2009 Iván Dimitri Marroquín; 1Formerly McGill University, Earth & Planetary Sciences Department, Montréal, Canada; presently Paradigm, Houston, Texas, U.S.A. E-mail: [email protected]. Search for other works by this author on: Jean-Jules Brault; 2École Polytechnique Montréal, Département de Génie électrique, Campus de 1’Université de Montréal, Montréal, Canada. E-mail: [email protected]. Search for other works by this author on: Bruce S. Hart 3Formerly McGill University, Earth & Planetary Sciences Department, Montréal, Canada; presently ConocoPhillips, Houston, Texas, U.S.A. E-mail: [email protected]. Search for other works by this author on: Iván Dimitri Marroquín 1Formerly McGill University, Earth & Planetary Sciences Department, Montréal, Canada; presently Paradigm, Houston, Texas, U.S.A. E-mail: [email protected]. Jean-Jules Brault 2École Polytechnique Montréal, Département de Génie électrique, Campus de 1’Université de Montréal, Montréal, Canada. E-mail: [email protected]. Bruce S. Hart 3Formerly McGill University, Earth & Planetary Sciences Department, Montréal, Canada; presently ConocoPhillips, Houston, Texas, U.S.A. E-mail: [email protected]. Publisher: Society of Exploration Geophysicists Received: 23 Oct 2007 Revision Received: 11 Jun 2008 Revision Requested: 31 Dec 2008 First Online: 13 Jul 2017 Online ISSN: 1942-2156 Print ISSN: 0016-8033 © 2009 Society of Exploration Geophysicists Geophysics (2009) 74 (1): P1–P11. Article history Revision Received: 11 Jun 2008 Revision Requested: 31 Dec 2008 First Online: 13 Jul 2017 Cite View This Citation Add to Citation Manager Search Site Abstract Seismic facies analysis aims to identify clusters (groups) of similar seismic trace shapes, where each cluster can be considered to represent variability in lithology, rock properties, and/or fluid content of the strata being imaged. Unfortunately, it is not always clear whether the seismic data has a natural clustering structure. Cluster analysis consists of a family of approaches that have significant potential for classifying seismic trace shapes into meaningful clusters. The clustering can be performed using a supervised process (assigning a pattern to a predefined cluster) or an unsupervised process (partitioning a collection of patterns into groups without predefined clusters). We evaluate and compare different unsupervised clustering algorithms (e.g., partition, hierarchical, probabilistic, and soft competitive models) for pattern recognition based entirely on the characteristics of the seismic response. From validation results on simple data sets, we demonstrate that a self-organizing maps algorithm implemented in a visual data-mining approach outperforms all other clustering algorithms for interpreting the cluster structure. We apply this approach to 2D seismic models generated using a discrete, known number of different stratigraphic geometries. The visual
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