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  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.

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http://www.theses.fr/2017GREAU013 (Ergänzendes Material)
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https://tel.archives-ouvertes.fr/tel-01652115 (Verlagsversion)
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 Urheber:
Derras, Boumédiène1, Autor
2.6 Seismic Hazard and Stress Field, 2.0 Geophysics, Departments, GFZ Publication Database, Deutsches GeoForschungsZentrum, Autor              
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1External Organizations, ou_persistent22              

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 Zusammenfassung: 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

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Sprache(n): fra - Französisch
 Datum: 2017
 Publikationsstatus: Final veröffentlicht
 Seiten: 257
 Ort, Verlag, Ausgabe: Saint-Martin-d'Hères : Université Grenoble Alpes
 Inhaltsverzeichnis: -
 Art der Begutachtung: -
 Identifikatoren: GFZPOF: p3 PT4 Natural Hazards
 Art des Abschluß: Doktorarbeit

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