Computer Science – Performance
Scientific paper
Dec 2009
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2009ep%26s...61.1275s&link_type=abstract
Earth, Planets and Space, Volume 61, p. 1275-1285.
Computer Science
Performance
Scientific paper
Determination of earthquake magnitude from the initial P-wave portion of ground motion is the key problems for Earthquake Early Warning (EEW). We analyzed performance of scaling relations between one of the frequently used early warning parameters, so-called characteristic period τC and moment magnitude MW in respect of (a) characteristics of datasets accumulated in various regions (earthquake depth, characteristics of network) and (b) variation of initial conditions applying for determination of the parameter τC (length of P-wave window, number of used stations). The used data contain strong-motion records from 110 earthquakes (moment magnitude range 4.4-7.6) occurred in Japan and Taiwan. We show that, although the standard error of regression τC = f( MW) in general becomes smaller with the increase of the length of P-wave window (PL) and the number (N) of averaged observations (stations), the uncertainty in estimation of MW given observed τC does not decrease further for PL > 3-4 seconds and N > 3-4 stations. The information about earthquake depth also plays an important role in reducing the uncertainty of magnitude estimations. The table of confidence limits for estimations of the moment magnitude hat{M}W based on observations of the characteristic period τC is provided.
Furumura Takashi
Sokolov Valentin V.
Wenzel Friedemann
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