Bulletin of Earthquake Engineering

, Volume 12, Issue 1, pp 495–516 | Cite as

Towards fully data driven ground-motion prediction models for Europe

  • Boumédiène Derras
  • Pierre Yves Bard
  • Fabrice Cotton
Original Research Paper

Abstract

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 \(V_{s30}\) properties have been selected. Five input parameters were selected: the moment magnitude \(M_{W}\), the Joyner–Boore distance \(R_{JB}\), the focal mechanism, the hypocentral depth, and the site proxy \(V_{S30}\). 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 (\(\sigma \)) 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.

Keywords

Neural networks ground motion RESORCE Pseudo-Spectral Acceleration \(\sigma \) 

Supplementary material

10518_2013_9481_MOESM1_ESM.xlsx (77 kb)
Supplementary material 1 (xlsx 77 KB)
10518_2013_9481_MOESM2_ESM.docx (182 kb)
Supplementary material 2 (docx 182 KB)

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Copyright information

© Springer Science+Business Media Dordrecht 2013

Authors and Affiliations

  • Boumédiène Derras
    • 1
  • Pierre Yves Bard
    • 2
  • Fabrice Cotton
    • 3
  1. 1.Risk Assessment and Management Laboratory (RISAM)Université Abou Bekr Belkaïd, Faculté de Technologie. BP 230-13048TlemcenAlgerie
  2. 2.Institut de Sciences de la Terre (ISTerre)Université Joseph FourierGrenoble cedex 9France
  3. 3.Institut de Sciences de la Terre (ISTerre)Université Joseph FourierGrenoble cedex 9France

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