Unsupervised feature selection and general pattern discovery using Self-Organizing Maps for gaining insights into the nature of seismic wavefields

Computers and Geosciences

Volumn 35, Issue 9, 2009, Pages 1757-1767

  Köhler, Andreas ^a   Ohrnberger, Matthias ^a   Scherbaum, Frank ^a  

^a UNIVERSITY OF POTSDAM   (Germany)

Author keywords

Clustering; Data mining; Feature selection; Seismic features; Unsupervised learning

Indexed keywords

CLUSTERING; CLUSTERING PROCEDURE; DATA-DRIVEN; EARTHQUAKE RECORDING; FEATURE SELECTION; FEATURE SELECTION METHODS; FEATURE SUBSET; FEATURE VECTORS; GAINING INSIGHTS; GENERAL PATTERNS; HIGH DIMENSIONAL DATA; LEARNING APPROACH; LOVE WAVE; OR-NETWORKS; PARAMETRIZATIONS; RAYLEIGH; REAL WORLD DATA; RUNS TESTS; SEISMIC DATAS; SEISMIC FEATURES; SEISMIC WAVEFIELDS; SIGNIFICANCE TESTING; SPECTRAL ANALYSIS; STANDARD ANALYSIS; TEMPORAL PATTERN; TIME WINDOWS; UNSUPERVISED FEATURE SELECTION; VISUALIZATION TECHNIQUE; WAVE FORMS; WAVE NUMBERS; WAVEFIELDS;

CONFORMAL MAPPING; DATA VISUALIZATION; FEATURE EXTRACTION; MINING; SEISMOLOGY; SELF ORGANIZING MAPS; SPECTRUM ANALYSIS; SPECTRUM ANALYZERS; SPEECH RECOGNITION; UNSUPERVISED LEARNING; VISUALIZATION;

CLUSTER ANALYSIS;

ARTIFICIAL NEURAL NETWORK; CLUSTER ANALYSIS; DATA MINING; DATA SET; FREQUENCY ANALYSIS; LEARNING; LOVE WAVE; PARAMETERIZATION; RAYLEIGH WAVE; SEISMIC DATA; SEISMIC WAVE; SPECTRAL ANALYSIS; TEMPORAL VARIATION; VISUALIZATION; WAVE FIELD;

EID: 68749092107     PISSN: 00983004     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cageo.2009.02.004     Document Type: Article

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