Sentence examples for swift estimation from inspiring English sources
The Mentawai tsunami earthquake was estimated as M w 7.5 by the SWIFT estimation, which is significantly smaller than M w ~ 7.8 as derived from the GCMT and USGS solutions (Fig. 3e).
SWIFT estimates one-dimensional kernel density functions for each of the d observation dimensions and d principal components of, where the optimal smoothing parameter for the kernel density estimation procedure is determined in a data-dependent manner using the normal optimal smoothing method 24.
Although the automatic SWIFT CMT estimation procedure proposed in the present study is intended for large earthquakes (M w > 7), the procedure may still have difficulty in reasonably evaluating the size of tsunami earthquakes.
This as a result can not only alleviate local minima and convergence issues but can also enable swift and precise parameter estimation to assist optimal PV design and augmented ECR performance.
The estimation triggers the SWIFT system to automatically calculate CMT and source time function of the detected earthquake (Bonita et al. 2015; Punongbayan et al. 2015).
Therefore, emergency shut-off decision is vital and earthquake damage estimation can be effectively used for a more reliable and swift decision-making.
We have recently implemented an automatic CMT estimation procedure for large earthquakes (M w > 7) into the previous SWIFT system (Nakano et al. 2008) and have incorporated a tsunami forecast system based on the estimated CMT.
Within the framework of an automatic/manual CMT estimation called the source-parameter determinations based on waveform inversion of Fourier Transformed seismograms (SWIFT) system (Nakano et al. 2008, 2010), the NIED has routinely analyzed the observed seismic data and maintained a database of the estimated CMTs of earthquakes that have occurred throughout the regional seismic networks.
