Fayaz, J; Galasso, C; (2022) A Generalized Ground Motion Model for consistent Mainshock-Aftershock Ground Motion Intensity Measures using Deep Neural Networks. In: Proceedings of the 12th National Conference in Earthquake Engineering. Earthquake Engineering Research Institute: Salt Lake City, UT, USA.

Text Fayaz&Galasso - A Generalized Ground Motion Model for consistent Mainshock-Aftershock Ground Motion Intensity Measures using Deep Neural Networks.pdf - Accepted Version Access restricted to UCL open access staff until 14 February 2032. Download (565kB)

Abstract

Utilization of “consistent” mainshock (MS) - aftershock (AS) ground motions is desirable in practical engineering applications. Such consistency in selecting MSAS sequences requires proper consideration of the correlations between and within the intensity measures of MS and AS ground motions. This study proposes a generalized ground motion model (GGMM) to estimate consistent 30×1 vectors of intensity measures for mainshocks (denoted as IMMS) and aftershocks (denoted as IMAS) using a framework of successive long-short-term-memory (LSTM) recurrent neural network (RNN). The vectors of IMMS and IMAS consists of geometric means of significant duration (D5−95, geom), Arias intensity (lageom), cumulative absolute velocity (CAVgeom), peak ground velocity (PGVgeom), peak ground acceleration (PGAgeom) and RotD50 spectral acceleration (Sa(T)) at 25 periods for both MS and AS ground motions. The proposed RNN-based framework is trained on a carefully selected set of ~700 crustal and subduction recorded MSAS sequences. The inputs to the framework include a 5×1 vector of source and site parameters for mainshock and aftershock recordings. The residuals of the trained LSTM-based RNNs are further used to develop empirical covariance structures for IMMS and IMAS.

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