An Examination of Some New and Classical Short Period Discriminants

Shumway, Robert H.; Blandford, R. R.

doi:10.21236/ada007051

Cited by 7 publications

(4 citation statements)

References 6 publications

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“…The rationale here is that potential high-frequency discrimination power is lost (attenuated) for propagation paths exceeding a few hundred kilometres. As also demonstrated here, some low-frequency P-signal discriminants are important, as apparently first reported by Shumway & Blandford (1974) and more recently by Taylor & Marshall (1991).…”

Section: Discussionsupporting

confidence: 83%

“…9. Naturally, removal of low-frequency noise is important as both theoretical and observational studies demonstrate that part of the discrimination power is vested in the low/high-frequency bands of the P signal (Shumway & Blandford 1974;Evernden et al 1986;Taylor & Marshall 1991). In the Tjcistheim & Husebye (1976) and Sandvin & Tjcistheim (1978) studies, the noise suppression was by beamforming, which for the large-aperture NORSAR array was efficient due to the much larger sensor spacing here.…”

Section: E V E N T Selection a N D N O R E S S R E C O R D Pre-procesmentioning

confidence: 99%

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Enhanced Seismic Source Discrimination Using Noress Recordings From Eurasian Events

Tsvang

Pinsky

Husebye

1993

Geophysical Journal International

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S U M M A R YThe problem of discriminating between earthquakes and underground nuclear explosions is formulated as an exercise in pattern recognition approach analysis. An advantage of our procedure is flexibility, by combining both adaptive noise suppression and event classification incorporating feature selection criteria.The analysis has been applied to a learning set of 44 nuclear explosions (eight test sites) and 35 earthquakes in Eurasia recorded at the NORESS array ( Fig. 1). The signal features considered were the normalized power in eight spectral bands in the 0.2-5.0 Hz range of the P wave (6 s) and the P coda (30 s). Physically, it means that we exploit potential differences in the shape of earthquake and explosion spectra, respectively. Other features included are peak P and P-coda amplitude frequencies and relative PIP-coda power. These 19 features were extracted either from conventional array beam traces or the optimum group filtered traces (OGFremoval of coherent low-frequency noise). Using the feature selection algorithm, based on estimates of the expected probability of misclassification (EPMC), only two to four features were needed for optimum discrimination performance. The dominant features were coda excitation and P-and P-coda power at lower signal frequencies. Furthermore, feature parameters extracted from the OGF traces had a slightly better performance in comparison to those extracted from beam traces. Finally, there were no misclassifications for OGF-derived features when the explosion population was limited to East Kazakh events, while including events from the other test sites lead to a decrease in discrimination power.

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Section: Discussionsupporting

confidence: 83%

Section: E V E N T Selection a N D N O R E S S R E C O R D Pre-procesmentioning

confidence: 99%

Enhanced Seismic Source Discrimination Using Noress Recordings From Eurasian Events

Tsvang

Pinsky

Husebye

1993

Geophysical Journal International

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“…In addition to the work cited above, studies which have restricted themselves to a single array, e.g., LASA or NORSAR, and to a basically statistical approach have found that a time-series type of definition of complexity serves as a useful discriminant between earthquakes and explosions, especially in combination with the observed deficit of explosion energy below 1 Hz as compared to the energy near 1 Hz, (note not an excess of energy above 1 Hz) see, for example, Shumway andBlandford (1974) andTsvang et. al.…”

Section: List Of Figuresmentioning

confidence: 99%

Discrimination of Earthquakes and Explosions at Regional Distances Using Complexity

Blanford¹

1993

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“…Furthermore, in some cases, they play a main role and have been extensively studied in statistical literature. Some particular topics studied are the discrimination of the origin of seismic records (see Tojstheim, 1975;Shumway and Blandford;Kakizawa et al, 1998), the discrimination between levels of anesthesia (Gersch et al, 1979), the analysis of electroencephalographic data (see Gevins et al, 1975;Alagon, 1989) and the classification of models of industrial production series (Piccolo, 1990).…”

Section: Introductionmentioning

confidence: 99%

Discriminant and cluster analysis for Gaussian stationary processes: local linear fitting approach

Vilar

Pértega

2004

Journal of Nonparametric Statistics

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This article is concerned with discrimination and clustering of Gaussian stationary processes. The problem of classifying a realization X n = (X 1 , . . . , X n ) t from a linear Gaussian process X into one of two categories described by their spectral densities f 1 (λ) and f 2 (λ) is considered first. A discrimination rule based on a general disparity measure between every f i (λ), i = 1, 2, and a nonparametric spectral density estimatorf n (λ) is studied when local polynomial techniques are used to obtainf n (λ). In particular, three different local linear smoothers are considered. The discriminant statistic proposed here provides a consistent classification criterion for all three smoothers in the sense that the misclassification probabilities tend to zero. A simulation study is performed to confirm in practice the good theoretical behavior of the discriminant rule and to compare the influence of the different smoothers. The disparity measure is also used to carry out cluster analysis of time series and some examples are presented and compared with previous works.

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An Examination of Some New and Classical Short Period Discriminants

Cited by 7 publications

References 6 publications

Enhanced Seismic Source Discrimination Using Noress Recordings From Eurasian Events

Enhanced Seismic Source Discrimination Using Noress Recordings From Eurasian Events

Discrimination of Earthquakes and Explosions at Regional Distances Using Complexity

Discriminant and cluster analysis for Gaussian stationary processes: local linear fitting approach

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