Towards fully data driven ground-motion prediction models for Europe
We have used the Artificial Neural Network method (ANN) for the derivation of physically sound, easy-to-handle, predictive ground-motion models from a subset of the Reference database for Seismic ground-motion prediction in Europe (RESORCE). Only shallow earthquakes (depth smaller than 25 km) and re...
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Veröffentlicht in: | Bulletin of earthquake engineering 2014-02, Vol.12 (1), p.495-516 |
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Format: | Artikel |
Sprache: | eng |
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Zusammenfassung: | We have used the Artificial Neural Network method (ANN) for the derivation of physically sound, easy-to-handle, predictive ground-motion models from a subset of the Reference database for Seismic ground-motion prediction in Europe (RESORCE). Only shallow earthquakes (depth smaller than 25 km) and recordings corresponding to stations with measured
V
s
30
properties have been selected. Five input parameters were selected: the moment magnitude
M
W
, the Joyner–Boore distance
R
J
B
, the focal mechanism, the hypocentral depth, and the site proxy
V
S
30
. A feed-forward ANN type is used, with one 5-neuron hidden layer, and an output layer grouping all the considered ground motion parameters, i.e., peak ground acceleration (
PGA
), peak ground velocity (
PGV
) and 5 %-damped pseudo-spectral acceleration (
PSA
) at 62 periods from 0.01 to 4 s. A procedure similar to the random-effects approach was developed to provide between and within event standard deviations. The total standard deviation (
σ
) varies between 0.298 and 0.378 (log
10
unit) depending on the period, with between-event and within-event variabilities in the range 0.149–0.190 and 0.258–0.327, respectively. Those values prove comparable to those of conventional GMPEs. Despite the absence of any a priori assumption on the functional dependence, our results exhibit a number of physically sound features: magnitude scaling of the distance dependency, near-fault saturation distance increasing with magnitude, amplification on soft soils and even indications for nonlinear effects in softer soils. |
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ISSN: | 1570-761X 1573-1456 |
DOI: | 10.1007/s10518-013-9481-0 |