Spectral Ground Motion Models for Himalayas Using Transfer Learning Technique

Abstract

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.