English
 
Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Estimation des mouvements sismiques et de leur variabilité par approche neuronale : Apport à la compréhension des effets de la source, de propagation et de site

Derras, B. (2017): Estimation des mouvements sismiques et de leur variabilité par approche neuronale: Apport à la compréhension des effets de la source, de propagation et de site, PhD Thesis, Saint-Martin-d'Hères : Université Grenoble Alpes, 257 p.

Item is

Files

show Files

Locators

show
hide
Locator:
http://www.theses.fr/2017GREAU013 (Supplementary material)
Description:
-
Locator:
https://tel.archives-ouvertes.fr/tel-01652115 (Publisher version)
Description:
-

Creators

show
hide
 Creators:
Derras, Boumédiène1, Author
2.6 Seismic Hazard and Stress Field, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, Author              
Affiliations:
1External Organizations, ou_persistent22              

Content

show
hide
Free keywords: -
 Abstract: This thesis is devoted to an in-depth analysis of the ability of "Artificial Neural Networks" (ANN) to achieve reliable ground motion predictions. A first important aspect concerns the derivation of "GMPE" (Ground Motion Prediction Equations) with an ANN approach, and the comparison of their performance with those of "classical" GMGEs derived on the basis of empirical regressions with pre-established, more or less complex, functional forms. To perform such a comparison involving the two "betweeen-event" and "within-event" components of the random variability, we adapt the algorithm of the "random effects model" to the neural approach. This approach is tested on various, real and synthetic, datasets: the database compiled from European, Mediterranean and Middle Eastern events (RESORCE: Reference database for Seismic grOund-motion pRediction in Europe), the database NGA West 2 (Next Generation Attenuation West 2 developed in the USA), and the Japanese database derived from the KiK-net accelerometer network. In addition, a comprehensive set of synthetic data is also derived with a stochastic simulation approach. The considered ground motion parameters are those which are most used in earthquake engineering (PGA, PGV, response spectra and also, in some cases, local amplification functions). Such completely "data-driven" neural models, inform us about the respective, and possibly coupled, influences of the amplitude decay with distance, the magnitude scaling effects, and the site conditions, with a particular focus on the detection of non-linearities in site response. Another important aspect is the use of ANNs to test the relevance of different site proxies, through their ability to reduce the random variability of ground motion predictions. The ANN approach allows to use such site proxies either individually or combined, and to investigate their respective impact on the various characteristics of ground motion. The same section also includes an investigation on the links between the non-linear aspects of the site response and the different site proxies. Finally, the third section focuses on a few source-related effects: analysis of the influence of the "style of faulting" on ground motion, and, indirectly, the dependence between magnitude and seismic stress drop

Details

show
hide
Language(s): fra - French
 Dates: 2017
 Publication Status: Finally published
 Pages: 257
 Publishing info: Saint-Martin-d'Hères : Université Grenoble Alpes
 Table of Contents: -
 Rev. Type: -
 Identifiers: GFZPOF: p3 PT4 Natural Hazards
 Degree: PhD

Event

show

Legal Case

show

Project information

show

Source

show