Decision-theoretic approach for classification of Ricker wavelets and detection of seismic anomalies

Journal Article · Sun Mar 01 04:00:00 EST 1987 · IEEE Trans. Geosci. Remote Sens.; (United States)

DOI:https://doi.org/10.1109/TGRS.1987.289721· OSTI ID:5542673

Huang, K Y; Fu, K S

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.

• Research Organization: Dept. of Computer Science, School of Electrical Engineering, Univ. of Houston - University Park, Houston, TX 77004
• OSTI ID: 5542673
• Journal Information: IEEE Trans. Geosci. Remote Sens.; (United States), Journal Name: IEEE Trans. Geosci. Remote Sens.; (United States) Vol. GE-25:2; ISSN IGRSD
• Country of Publication: United States
• Language: English

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580203 -- Geophysics-- Geophysical Survey Methods-- (1980-1989)
CLASSIFICATION
COMPUTERS
DETECTION
EXPLORATION
GEOLOGIC DEPOSITS
GEOPHYSICAL SURVEYS
GEOPHYSICS
MINERAL RESOURCES
NATURAL GAS DEPOSITS
PATTERN RECOGNITION
PETROLEUM DEPOSITS
RESOURCES
SEISMIC DETECTION
SEISMIC SURVEYS
SEISMIC WAVES
SURVEYS
TRAINING
WAVE FORMS
WAVE PROPAGATION