Ground motion model using simulated scenario earthquake records in Azores Plateau (Portugal) at bedrock

The Azores archipelago in Portugal, located within a seismically active region, experienced several moderate to strong earthquakes throughout its history, including significant events in 1980 (moment magnitude, M w 6.9) and 1998 ( M w 6.2). Despite its moderate to high seismicity, the region lacks a comprehensive database of recorded ground motions due to limited instrumental seismic data. To address this gap, ground motion simulation techniques provide alternative region-specific time series for areas with sparse seismic networks or a lack of catastrophic earthquake events. This study develops a region-specific ground motion model (GMM) for the Azores Plateau in Portugal, utilizing a homogeneous dataset of region-specific simulated records that have been generated in the bedrock through a stochastic finite-fault approach. The GMM is constructed using a mixed-effects algorithm to predict peak ground acceleration, peak ground velocity, and spectral acceleration ordinates at periods between 0.02 and 2.0 s. The model utilizes input parameters for prediction, including M w , Joyner-Boore distance ( R JB ), and focal depth ( F D ). The model is formulated for shallow seismic events ranging from magnitude M w 5.0 to 6.8, F D 5–17 km, and R JB up to 150 km on bedrock sites. Uncertainty quantification is performed through residual analysis, offering insights into inter-event and intra-event variabilities. The results demonstrate that the proposed model effectively predicts ground motion parameters across the considered range of magnitudes, distances, and periods, providing a valuable tool for assessing the earthquake hazard in the Azores region. • Region-specific GMM in bedrock for the Azores Plateau via stochastic simulations. • Prediction of PGA, PGV, and spectral accelerations ranging from 0.02 to 2.0 s. • Simulated ground motions in Azores region decay faster than global models. • Quantified uncertainties show unbiased residuals validating model's reliability.