ANN-based ground motion model for Turkey using stochastic simulation of earthquakes

Turkey is characterized by a high level of seismic activity attributed to its complex tectonic structure. The country has a dense network to record earthquake ground motions; however, to study previous earthquakes and to account for potential future ones, ground motion sim- ulations are required. Ground motion simulation techniques offer an alternative means of generating region-specific time-series data for locations with limited seismic networks or re- gions with seismic data gaps, facilitating the study of potential catastrophic earthquakes. In this research, a local ground motion model (GMM) for Turkey is developed using region- specific simulated records, thus constructing a homogeneous data set. The simulations employ the stochastic finite-fault approach and utilize validated input-model parameters in distinct re- gions, namely Afyon, Erzincan, Duzce, Istanbul and Van. To overcome the limitations of linear regression-based models, artificial neural network is used to establish the form of equations and coefficients. The predictive input parameters encompass fault mechanism (FM), focal depth (FD), moment magnitude (Mw), Joyner and Boore distance (RJB) and average shear wave velocity in the top 30 m (Vs30). The data set comprises 7359 records with Mw ranging between 5.0 and 7.5 and RJB ranging from 0 to 272 km. The results are presented in terms of spectral ordinates within the period range of 0.03–2.0 s, as well as peak ground acceleration and peak ground velocity. The quantification of the GMM uncertainty is achieved through the analysis of residuals, enabling insights into inter- and intra-event uncertainties. The simulation results and the effectiveness of the model are verified by comparing the predicted values of ground motion parameters with the observed values recorded during previous events in the region. The results demonstrate the efficacy of the proposed model in simulating physical phenomena.This work was partly financed by FCT/MCTES through National funds (PIDDAC) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under reference UIDB/04029/2020, and under the Associate Laboratory Advanced Production and Intelligent Systems ARISE under refer ence LA/P/0112/2020. This study has been partly funded by the STAND4HERITAGE project that has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program (grant agreement no. 833123), as an advanced grant. This work is financed by national funds through FCT—Foundation for Science and Technology, under grant agreement 2020.08876.BD attributed to the second author. This work is financed by national funds through FCT—Foundation for Science and Technology, under grant agreement UI/BD/153379/2022 attributed to the third author. Shaghayegh Karimzadeh: Conceptualisation, Data curation, Formal analysis, Investigation, Methodology, Resources, Supervision, Validation, Visualisation, Writing—original draft, Writing—review & editing. Amirhossein Mohammadi: Formal analysis, Investigation, Methodology, Resources, Visualisation, Writing—original draft, Writing—review & editing. Sayed Mohammad Sajad Hussaini: Formal anal ysis, Investigation, Writing—original draft, Writing—review & editing. Daniel Caicedo: Formal analysis, Investigation, Writing— original draft, Writing—review & editing. Aysegul Askan: Data curation, Resources, Writing—review & editing. Paulo B. Lourenço: Funding acquisition, Resources, Supervision, Writing—review & editing