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

• Autorzy: Vemula Sreenath , Raghukanth S. T. G. , Ponnalagu Alagappan

Identyfikatory

DOI

10.1007/s11600-021-00707-1

• Języki publikacji: EN

Abstrakty

EN

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

EN

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

PL

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

• 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

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Uwagi

PL

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).