Principal Approach of PreSEIS On-Site

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: Principal approach of PreSEIS On-site. (a) The algorithm uses the logarithmic values of the integrated absolute amplitudes of acceleration, velocity, and displacement waveform time series , and u(t) at a single sensor, as well as VS30 site characterization. Outputs are (1)simple earthquake/noise discrimination and near/far source classification, and estimates of (2) the moment magnitude M, (3) the epicentral distance Δ, and (4) the PGV. All estimates are updated with progressing time t0. (b) PreSEIS On-site uses two-layer-feed-forward (TLFF) neural networks composed of simple processing units arranged in input layers, hidden layers, and output layers. Ten TLFF networks, which form a so-called committee, are trained on the same task (e.g., the prediction of M) using slightly different training datasets; the median value taken over the outputs of all 10 TLFF networks defines the output of PreSEIS On-site. (Böse et al. 2012)

2.9 UrEDAS:
UrEDAS is an EEW system operational in Japan along the Shinkansen lines since 1992. UrEDAS estimates source parameters in real time from the data collected at a single station without the need for storage. The main functions of UrEDAS are estimation of the earthquake location, magnitude, vulnerability assessment and issuing of warning if required. Like all other EEW systems UrEDAS uses the initial P wave motion for the estimation of the source parameters. UrEDAS recognises a P wave from an S wave by observing the vertical amplitude to

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