Decision-Theoretic Approach for Classification of Ricker Wavelets and Detection of Seismic Anomalies

Kou-Yuan Huang, King Sun Fu

Research output: Contribution to journalArticle

7 Scopus citations

Abstract

Decision-theoretic pattern recognition methods are applied to classifying Ricker wavelets and to detecting waveform anomalies in seismograms. The methods include Bayes decision rule and linear and quadratic classifications. Envelope and instantaneous frequency are extracted as the two features of a seismic trace used as input into the classification schemes. A modified fixed-increment training procedure is employed to solve the decision boundary. The classification schemes successfully distinguish zero-phase Ricker wavelets of different peak frequencies from each other and from random noise.

Original languageEnglish
Pages (from-to)118-123
Number of pages6
JournalIEEE Transactions on Geoscience and Remote Sensing
VolumeGE-25
Issue number2
DOIs
StatePublished - 1 Jan 1987

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