Relationship between displacement and velocity amplitudes of seismic waves from local earthquakes

Katsumata, Akio

doi:10.1186/bf03352391

Cited by 6 publications

(3 citation statements)

References 23 publications

(21 reference statements)

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“…(1) is greater for velocity than for displacement. This relationship has been seen previously for short-period amplitude measurements (Watanabe, 1971;Katsumata, 2001).…”

Section: Methods and Datasupporting

confidence: 86%

Rapid magnitude determination from peak amplitudes at local stations

Katsumata¹,

Ueno²,

Aoki³

et al. 2013

Earth Planet Sp

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The rapid determination of its magnitude soon after a great earthquake is necessary for the issuing of effective tsunami warnings, as demonstrated in the great earthquake off Tohoku district in Japan on March 11, 2011. The earthquake magnitude for the first tsunami warning was underestimated due to magnitude saturation. This paper proposes a method to determine magnitude rapidly from peak velocity and displacement of long-period seismic waves up to 100 seconds at local stations. When waveform data at local stations are available, the magnitude from S-wave peaks is expected to be determined faster than that from only P-wave peaks. It takes about 140 seconds to estimate a magnitude of about 9 for the March 11, 2011, earthquake, which would enable us to issue the first tsunami warning within three minutes after the same type of earthquake.

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“…(1) is greater for velocity than for displacement. This relationship has been seen previously for short-period amplitude measurements (Watanabe, 1971;Katsumata, 2001).…”

Section: Methods and Datasupporting

confidence: 86%

Rapid magnitude determination from peak amplitudes at local stations

Katsumata¹,

Ueno²,

Aoki³

et al. 2013

Earth Planet Sp

Self Cite

View full text Add to dashboard Cite

show abstract

“…The acceleration shows a similar behavior as the velocity, but nearly completely saturates above M A > 6.0. The saturation effects for velocity and acceleration have previously been observed by Katsumata (2001) and are due to the shifted frequency spectra. Interestingly, the variance of the acceleration magnitude is highest among the scales, suggesting a high variability of peak acceleration compared to peak displacement.…”

Section: Relations Between the Scalessupporting

confidence: 51%

Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile

Münchmeyer

Bindi

Sippl

et al. 2019

Geophysical Journal International

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SUMMARY Magnitude estimation is a central task in seismology needed for a wide spectrum of applications ranging from seismicity analysis to rapid assessment of earthquakes. However, magnitude estimates at individual stations show significant variability, mostly due to propagation effects, radiation pattern and ambient noise. To obtain reliable and precise magnitude estimates, measurements from multiple stations are therefore usually averaged. This strategy requires good data availability, which is not always given, for example for near real time applications or for small events. We developed a method to achieve precise magnitude estimations even in the presence of only few stations. We achieve this by reducing the variability between single station estimates through a combination of optimization and machine learning techniques on a large catalogue. We evaluate our method on the large scale IPOC catalogue with >100 000 events, covering seismicity in the northern Chile subduction zone between 2007 and 2014. Our aim is to create a method that provides low uncertainty magnitude estimates based on physically meaningful features. Therefore we combine physics based correction functions with boosting tree regression. In a first step, we extract 110 features from each waveform, including displacement, velocity, acceleration and cumulative energy features. We correct those features for source, station and path effects by imposing a linear relation between magnitude and the logarithm of the features. For the correction terms, we define a non-parametric correction function dependent on epicentral distance and event depth and a station specific, adaptive 3-D source and path correction function. In a final step, we use boosting tree regression to further reduce interstation variance by combining multiple features. Compared to a standard, non-parametric, 1-D correction function, our method reduces the standard deviation of single station estimates by up to $57\, {\rm per\, cent}$, of which $17\, {\rm per\, cent}$ can be attributed to the improved correction functions, while boosting tree regression gives a further reduction of $40\, {\rm per\, cent}$. We analyse the resulting magnitude estimates regarding their residuals and relation to each other. The definition of a physics-based correction function enables us to inspect the path corrections and compare them to structural features. By analysing feature importance, we show that envelope and P wave derived features are key parameters for reducing uncertainties. Nonetheless the variety of features is essential for the effectiveness of the boosting tree regression. To further elucidate the information extractable from a single station trace, we train another boosting tree on the uncorrected features. This regression yields magnitude estimates with uncertainties similar to the single features after correction, but without using the earthquake location as required for applying the correction terms. Finally, we use our results to provide high precision magnitudes and their uncertainties for the IPOC catalogue.

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“…Other interesting investigations concerning the application of Chebyshev Type I filter (8th order) and Bessel (3rd order) have been made by Stewart et al (2001) and Katsumata (2001), respectively. Authors Stewart et al (2001), in their PEER Report, use Chebyshev filters for processing data from 154 earthquakes.…”

Section: Introductionmentioning

confidence: 99%

Influence of parameters selection in Chebyshev filters on the strong motion data processing

Mollova

Scherbaum

2007

Bull Earthquake Eng

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This contribution discusses the application of Chebyshev Type I filter for processing real earthquake records. Consideration is given to the effects of filtering parameters (passband amplitude ripple and order of the filter) on the time series, strong-motion parameters, Fourier Amplitude Spectrum of acceleration, and elastic displacement response spectra. Time histories of five earthquakes with different moment magnitudes have been examined (from stations located close to the epicenters). Data processing is based on application of bandpass Chebyshev filtering over frequency range with substantial signal to noise ratio (level of 3 or approximately 3 dB). Applying different filters, we have monitored several important strong-motion parameters: peak values of acceleration, velocity, and displacement; Arias intensity, acceleration/velocity spectrum intensity, significant duration, etc. Some new results and conclusions concerning the influence of Chebyshev filter in data processing of records have been summarized. The graphical and numerical outcomes obtained, as well as the comparison with a Butterworth causal filter, are included in the work. The results could be potentially useful to engineering seismologists who need to evaluate and better understand the merits of this type of filtering for strong-motion data processing.

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Relationship between displacement and velocity amplitudes of seismic waves from local earthquakes

Cited by 6 publications

References 23 publications

Rapid magnitude determination from peak amplitudes at local stations

Rapid magnitude determination from peak amplitudes at local stations

Low uncertainty multifeature magnitude estimation with 3-D corrections and boosting tree regression: application to North Chile

Influence of parameters selection in Chebyshev filters on the strong motion data processing

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