Optimization of Surface Wave Identification and Measurement

Stevens, J. L.; McLaughlin, K. L.

doi:10.1007/978-3-0348-8264-4_12

Cited by 25 publications

(61 citation statements)

References 18 publications

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“…In this case, the M s (VMAX) estimates are offset from Yacoub's (1983) estimates by approximately ‫30.0ם‬ m.u. Figure 11 shows the regression of the M s (VMAX) versus the Denny et al (1987Denny et al ( , 1989) m b for both the earthquake (Table 3) (Stevens and McLaughlin, 2001) suggest that convergence does occur. The classification equation based on the parallel-slope assumption becomes:…”

Section: Resultsmentioning

confidence: 94%

“…Some researchers believe that the available M s -m b datasets suggest that the two populations converge at smaller magnitudes (e.g., Stevens and McLaughlin, 2001). These researchers believe that the population convergence is caused by earthquake and explosion sources that become phenomenologically similar at smaller magnitudes.…”

Section: Discussionmentioning

confidence: 99%

“…However, because they did not consider frequency-dependent aspects of dispersion, the coefficient is not sufficient to account for dispersion effects at shorter periods (Bonner et al, 2003). The formula was adopted by the prototype International Data Center in 1998 for calculating surface-wave magnitudes at distances between 20 and 100 degrees; however, it is now used by the International Data Center to determine an M s for all surface waves recorded at distances less than 100 degrees (Stevens and McLaughlin, 2001). Russell (2006; Part I of this article) developed a timedomain method for measuring surface waves with minimum digital processing, using zero-phase Butterworth filters.…”

Section: Using the Equationmentioning

confidence: 99%

“…We then overlay the theoretical fundamental-and firsthigher mode dispersion curves predicted for the path from the global shear-wave model. We require overlap (similar to Stevens and McLaughlin, 2001) in the observed dispersion, plus error in the 8-to 25-sec period band, with the predicted fundamental-mode dispersion from the model.…”

Section: Surface-wave Identificationmentioning

confidence: 99%

“…We apply the correction to all events even though it was developed with a shallow explosion assumption. This is essentially the same procedure as Stevens and McLaughlin (2001), except that they used spectral instead of time-domain measurements and derived the source and receiver functions from earth models.…”

Section: Excitation Correctionmentioning

confidence: 99%

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Development of a Time-Domain, Variable-Period Surface-Wave Magnitude Measurement Procedure for Application at Regional and Teleseismic Distances, Part II: Application and Ms-mb Performance

Bonner¹,

Russell

Harkrider³

et al. 2006

Bulletin of the Seismological Society of America

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The Russell surface-wave magnitude formula, developed in Part I of this two-part article, and the M s (VMAX) measurement technique, discussed in this article, provide a new method for estimating variable-period surface-wave magnitudes at regional and teleseismic distances. The M s (VMAX) measurement method consists of applying Butterworth bandpass filters to data at center periods between 8 and 25 sec. The filters are designed to help remove the effects of nondispersed Airy phases at regional and teleseismic distances. We search for the maximum amplitude in all of the variable-period bands and then use the Russell formula to calculate a surface-wave magnitude.In this companion article, we demonstrate the capabilities of the method by using applications to three different datasets. The first application utilizes a dataset that consists of large earthquakes in the Mediterranean region. The results indicate that the M s (VMAX) technique provides regional and teleseismic surface-wave magnitude estimates that are in general agreement except for a small distance dependence of ‫200.0מ‬ magnitude units per degree. We also find that the M s (VMAX) estimates are less than 0.1 magnitude unit different than those from other formulas applied at teleseismic distances such as Rezapour and Pearce (1998) and Vanȇk et al. (1962).In the second and third applications of the method, we demonstrate that measurements of M s (VMAX) versus m b provide adequate separation of the explosion and earthquake populations at the Nevada and Lop Nor Test Sites. At the Nevada Test Site, our technique resulted in the misclassification of two earthquakes in the explosion population. We also determined that the new technique reduces the scatter in the magnitude estimates by 25% when compared with our previous studies using a calibrated regional magnitude formula. For the Lop Nor Test Site, we had no misclassified explosions or earthquakes; however, the data were less comprehensive.A preliminary analysis of Eurasian earthquake and explosion data suggest that similar slopes are obtained for observed M s (VMAX) versus m b data with m b Ͻ5. Thus the data are not converging at lower magnitudes. These results suggest that the discrimination of explosions from earthquakes can be achieved at lower magnitudes using the Russell (2006) formula and the M s (VMAX) measurement technique.

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Section: Resultsmentioning

confidence: 94%

Section: Discussionmentioning

confidence: 99%

Section: Using the Equationmentioning

confidence: 99%

Section: Surface-wave Identificationmentioning

confidence: 99%

Section: Excitation Correctionmentioning

confidence: 99%

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Development of a Time-Domain, Variable-Period Surface-Wave Magnitude Measurement Procedure for Application at Regional and Teleseismic Distances, Part II: Application and Ms-mb Performance

Bonner¹,

Russell

Harkrider³

et al. 2006

Bulletin of the Seismological Society of America

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Automated Detection, Extraction, and Measurement of Regional Surface Waves

Levshin

Ritzwoller

2001

Monitoring the Comprehensive Nuclear-Test-Ban Treaty: Surface Waves

111

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Yield Estimation from Surface-wave Amplitudes

Stevens¹,

Murphy²

2001

Pure appl. geophys.

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Ð Surface-wave amplitudes from explosion sources show less variation for a given event than body wave amplitudes, so it is natural to expect that yield estimates derived from surface waves will be more accurate than yield estimates derived from body waves. However, yield estimation from surface waves is complicated by the presence of tectonic strain release, which acts like one or more earthquake sources superimposed on top of the explosion. Moment-tensor inversion can be used to remove the tectonic component of the surface waves, however moment-tensor inversion for shallow sources is inherently non-unique so the explosion isotropic moment cannot be determined with the necessary accuracy by this means. Explosions on an island or near a mountain slope can exhibit anomalous surface waves similar to those caused by tectonic strain release. These complications cause yield estimates derived from surface waves to be less accurate than yield estimates from body waves recorded on a well-calibrated network with good coverage. Surface-wave amplitudes can be expressed as a surface-wave magnitude M s , which is de®ned as the logarithm of the amplitude plus a distance correction, or as a path corrected spectral magnitude, log M H 0 , which is derived from the surface-wave spectrum. We derive relations for M s vs. yield and log M H 0 vs. yield for a large data set and estimate the accuracy of these estimates.

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Optimization of Surface Wave Identification and Measurement

Cited by 25 publications

References 18 publications

Development of a Time-Domain, Variable-Period Surface-Wave Magnitude Measurement Procedure for Application at Regional and Teleseismic Distances, Part II: Application and Ms-mb Performance

Development of a Time-Domain, Variable-Period Surface-Wave Magnitude Measurement Procedure for Application at Regional and Teleseismic Distances, Part II: Application and Ms-mb Performance

Automated Detection, Extraction, and Measurement of Regional Surface Waves

Yield Estimation from Surface-wave Amplitudes

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