Title :
Fuzzy Clustering of Seismic Sequences: Segmentation of Time-Frequency Representations
Author_Institution :
Institute of Geophysics, University of Tehran, Tehran, IRAN
fDate :
5/1/2012 12:00:00 AM
Abstract :
Time-frequency (TF) representations based on minimum mean cross-entropy (MMCE) solution, Bessel kernel, and generalized marginal page distribution are used as the input of clustering. It is shown that general trends of frequency changed with time are not the same for these transforms. The proposed method is based on the knowledge integration of TF transforms followed by a clustering data matrix and the derivation of fuzzy membership values. Based on the output of cluster membership values, the full bandwidth illumination (FBWI) index is defined as a tool for qualitative seismic interpretation. The method is applicable for studying the frequency behavior of reflectors in a reservoir for detecting fluid migration paths.
Keywords :
Bessel functions; entropy; fuzzy set theory; geophysical fluid dynamics; geophysical signal processing; pattern clustering; seismology; sequences; Bessel kernel; FBWI index; MMCE solution; cluster membership values; clustering data matrix; fluid migration paths; full bandwidth illumination; fuzzy clustering; fuzzy membership values; generalized marginal page distribution; minimum mean cross-entropy; qualitative seismic interpretation; reflectors; reservoir; segmentation; seismic sequences; time-frequency representations; Clustering algorithms; Color; Fuzzy models; Geophysical measurements; Geophysical signal processing; Indexes; Shape analysis; Time frequency analysis;
Journal_Title :
Signal Processing Magazine, IEEE
DOI :
10.1109/MSP.2012.2185897