Fourier amplitude spectrum prediction and generation of synthetic ground motion to New Zealand

Vemula, Sreenath; Raghukanth, S. T. G.; Ponnalagu, Alagappan

doi:10.1007/s11600-021-00707-1

Artykuł - szczegóły

Tytuł artykułu

Fourier amplitude spectrum prediction and generation of synthetic ground motion to New Zealand

Autorzy

Vemula Sreenath , Raghukanth S. T. G. , Ponnalagu Alagappan

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-021-00707-1

Warianty tytułu

Języki publikacji

Abstrakty

Developing a ground motion model (GMM) for Fourier amplitude spectrum (FAS) is essential in seismology and engineering for generating response spectrum and synthetic time histories. Despite data-driven techniques being efficient in modeling complex relations, very few GMMs are developed for FAS using them. An efficient hybrid data-driven algorithm combining genetic algorithm and artificial neural network is implemented using the GeoNet database with 905 records from 77 events in the current work. The input parameters of the model are moment magnitude, Joyner–Boore distance, shear wave velocity, depth to the top of the rupture plane, fault, and tectonic fags. The developed FAS model is statistically tested to be robust and has good agreement with the recorded data and other available GMMs. The developed GMM to FAS has an overall correlation coefficient in the range of 0.8108–0.9298 and sigma in the range of 0.26–0.4 (in log10 units). Further, synthetic time histories are generated from the predicted FAS values and are consistent with various ground motion parameters and the response spectra.

Słowa kluczowe

ground-motion model GMMs artificial neural network ANN Fourier amplitude spectrum FAS synthetic ground motion

modele ruchu naziemnego GMM sztuczna sieć neuronowa ANN widmo amplitudy Fouriera FAS syntetyczny ruch naziemny

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 1

Strony

39--70

Opis fizyczny

Bibliogr. 44 poz.

Twórcy

autor

Vemula Sreenath

vsreenath2@gmail.com

Indian Institute of Technology Madras, Chennai, India

https://orcid.org/0000-0003-0819-6266

autor

Raghukanth S. T. G.

Indian Institute of Technology Madras, Chennai, India

autor

Ponnalagu Alagappan

Indian Institute of Technology Madras, Chennai, India

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-6c17d8e2-f036-4c8d-9e71-bb173fff28db