Estimation of seismic-wave parameters and signal detection using maximum-likelihood methods

A broad-band maximum likelihood method is presented for the detection of and parameter extraction from seismic events using wideband data recorded by an array of seismic stations. The statistical characteristics of finite Fourier-transformed data motivate the use of approximate maximum-likelihood (M...

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Bibliographic Details
Published in:Computers & geosciences 2001-03, Vol.27 (2), p.147-156
Main Authors: Chung, Pei-Jung, Jost, Michael L, Böhme, Johann F
Format: Article
Language:English
Subjects:
Applied geophysics
Earth sciences
Earth, ocean, space
Exact sciences and technology
GERESS array data
Internal geophysics
Maximum likelihood
Multiple phase detection
Parameter estimation
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Summary:A broad-band maximum likelihood method is presented for the detection of and parameter extraction from seismic events using wideband data recorded by an array of seismic stations. The statistical characteristics of finite Fourier-transformed data motivate the use of approximate maximum-likelihood (ML) methods which allow simultaneous detection and wave-parameter estimation. The detection strategy based on the likelihood ratio indicates the presence of a seismic event and resolves different phases of seismic events arriving within a time interval of interest. The corresponding slowness vectors of the phases are simultaneously estimated by optimization of the likelihood function over parameters of interest. The potential of the wideband ML method is demonstrated on GERESS data and compared to conventional f– k analysis showing advantages of the former in detection and resolution.
ISSN:0098-3004
1873-7803
DOI:10.1016/S0098-3004(00)00088-1