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.
|Publication status||Published - 2022|
|Event||12th National Conference on Earthquake Engineering, NCEE 2022 - Salt Lake City, United States|
Duration: 27 Jun 2022 → 1 Jul 2022
|Conference||12th National Conference on Earthquake Engineering, NCEE 2022|
|City||Salt Lake City|
|Period||27/06/22 → 1/07/22|
Bibliographical noteFunding Information:
This research is funded by the European Union's Horizon 2020 research and innovation program, specifically grant agreement number 821046: TURNkey “Towards more Earthquake-resilient Urban Societies through a Multi-sensorbased Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions.”
This research is funded by the European Union’s Horizon 2020 research and innovation program, specifically grant agreement number 821046: TURNkey “Towards more Earthquake-resilient Urban Societies through a Multi-sensor-based Information System enabling Earthquake Forecasting, Early Warning and Rapid Response actions.”
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