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ABSTRACT: We apply the 2D broadband maximum likelihood (ML) and interpolated
root-MUSIC methods to estimate the azimuth and velocity parameters of
teleseismic events recorded by the GERESS array. A sequential test based
on likelihood ratios (LR) is developed for signal detection. Our
experimental results show that both methods can provide reliable
estimates of signal parameters. However, ML is shown to have better
estimation accuracy and robustness than interpolated root-MUSIC at the
expense of a higher computational cost
Statistical Signal and Array Processing, 2000. Proceedings of the Tenth IEEE Workshop on; 02/2000
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ABSTRACT: A broad-band maximum likelihood method is presented for 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 can not only indicate the presence of a seismic
event but can also detect different phases of seismic events arriving
within a time interval of interest. The corresponding azimuths and
apparent velocities 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
Acoustics, Speech, and Signal Processing, 2000. ICASSP '00. Proceedings. 2000 IEEE International Conference on; 02/2000 · 4.63 Impact Factor
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ABSTRACT: A generalized likelihood ratio test is considered for testing
acoustic environmental models with application to parameter inversion
using an acoustic propagation code. In the following, we use the term
“hierarchy of models” to denote a sequence of model
structures M<sub>1</sub>, M<sub>2</sub>, … in which each
particular model structure M<sub>m</sub> contains all previous ones as
special cases. We propose a combined parameter estimation and multiple
sequential test for simultaneously determining the model order and its
parameters: given the observed data, how many parameters should be
included in the model? The last question is important for the order
selection problem in hierarchies of models with increasing number of
parameters where the observations are corrupted by additive noise. Monte
Carlo simulations show the behaviour of the sequential test for
selecting a model order as a function of the SNR. Finally, the test is
applied to broadband data measured using a vertical array near the
island of Elba in the Mediterranean Sea and compared with Akaike's
information criterion
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on; 05/1997 · 4.63 Impact Factor
