1. Spectral Ground Motion Models for Himalayas Using Transfer Learning Technique.
- Author
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Podili, Bhargavi, Basu, Jahnabi, and Raghukanth, STG
- Subjects
- *
ARTIFICIAL neural networks , *GROUND motion , *DATABASES , *MACHINE learning , *EARTHQUAKES - Abstract
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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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