Abstract
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.
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