Spectral Ground Motion Models for Himalayas Using Transfer Learning Technique

被引：3

作者：

Podili, Bhargavi ^{[1
]}

Basu, Jahnabi ^{[1
]}

Raghukanth, S. T. G. ^{[1
]}

机构：

[1] Indian Inst Technol Madras, Dept Civil Engn, Chennai, Tamil Nadu, India

来源：

JOURNAL OF EARTHQUAKE ENGINEERING | 2024年 / 28卷 / 13期

关键词：

Response spectra; Fourier amplitude spectra; effective amplitude spectra; transfer learning; deep neural network; Western Himalayas; ATTENUATION RELATIONS; PREDICTION EQUATIONS; EARTHQUAKE SEQUENCE; ACCELERATION; NEPAL; SIMULATIONS; GENERATION; COMPONENTS; GORKHA; BEWARE;

D O I：

10.1080/13632469.2024.2353261

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

Predicting robust earthquake spectra is challenging, especially for data sparse regions such as India. Often, alternatives to the traditional data-driven regression analysis are used to develop empirical models for such regions. Advancing these efforts, the present study aims at exploring an alternative machine learning technique called Transfer learning, wherein a non-parametric deep neural network is trained for response (Sa) and Fourier spectra (FAS) of Himalayas, which uses network parameters that were derived for a large comprehensive database (NGA-West2). While the FAS is derived using magnitude, distance, focal depth, and site class, the Sa is scaled using FAS and significant duration.

引用

页码：3623 / 3647

页数：25

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