Conditional generative adversarial network model for simulating intensity measures of aftershocks

Abstract

Earthquake disaster records demonstrate that the influence of aftershocks (ASs) needs to be considered in structural seismic design and performance assessment, owing to the additional damage caused by them. To study the failure mechanism of a structure undergoing a sequence earthquake (i.e., mainshock–aftershock (MS–AS)), a clear understanding of the correlation between the intensity measures (IMs) of an MS–AS is important. However, the above correlation has not yet been systematically studied. Previously, some researchers have investigated the correlation between the individual IMs of an MS–AS separately by a Copula function based on an assumption that all the IMs of the MS (AS) are independent. However, the IMs are actually related, owing to their definition. Concurrently, deep learning (particularly the generation models) can be used to reveal the potential connections between data without any assumptions. Moreover, these models can present the conditional probability distribution of data by adding specific conditions. This study aims to build a conditional generative adversarial network (CGAN) model to simulate the IMs of an AS of an MS–AS, which can not only reflect the correlation between the corresponding IMs of the MS–AS but also that between the IMs used. The performance of this model is ascertained based on residual analysis, and the IM AS predictability is tested using real records. This study selects 972 MS–AS ground motions from the Next Generation Attenuation-West2 (NGA-West2) database, randomly dividing them into 80% and 20% and using as the training set and testing set, respectively. The results show that the CGAN model can predict the IMs of ASs with good accuracy. Moreover, the ground motion prediction equation (GMPE) by Abrahamson et al. (ASK14) is selected to compare with the CGAN model, and it is exhibited that the CGAN model matches the as-recorded IMs of ASs better that the former. All these results demonstrate that the proposed CGAN model is a promising and reliable approach for IM prediction of an AS.