ANN-Based Ground Motion Model for the Azores Plateau (Portugal) Using Stochastic Ground Motion Simulations

Abstract:

This study develops an artificial neural network (ANN)-based ground motion model (GMM) for the Azores Plateau (Portugal) using a dataset generated through stochastic finite-fault simulations. The simulations are performed for both onshore and offshore rock-site scenarios, employing a dynamic corner-frequency algorithm. Randomized source and path parameters are incorporated to capture the aleatory variability of regional seismicity. The simulated ground motions are validated through a comprehensive statistical framework, confirming that the implemented randomization reproduces realistic variance and inter-period correlations observed in recorded data. The ANN-based GMM is trained using the simulated database to predict spectral acceleration across a wide range of magnitudes and source-to-site distances. The developed model and accompanying dataset together provide a reliable foundation for seismic hazard and risk assessments in the Azores Plateau region.

Keywords: Artificial neural network (ANN); Ground motion model (GMM); Stochastic finite-fault simulation; Onshore and offshore scenarios; Spectral acceleration prediction; Azores Plateau (Portugal).

Acknowledgments:

This work is financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.08982.CEECIND/CP2841/CT0033] attributed to the first author (https://doi.org/10.54499/2023.08982.CEECIND/CP2841/CT0033). This work was also supported by FCT/ Ministério da Ciência, Tecnologia e Ensino Superior (MCTES) under the R&D Unit Institute for Sustainability and Innovation in Structural Engineering (ISISE), under the references UID/4029/2025 (https://doi.org/10.54499/UID/04029/2025) and UID/PRR/04029/2025 (https://doi.org/10.54499/UID/PRR/04029/2025), and under the Associate Laboratory Advanced Production and Intelligent Systems (ARISE) under reference LA/P/0112/2020. This work is partly financed by national funds through FCT (Foundation for Science and Technology), under grant agreement [UI/BD/153379/2022] attributed to the second author. This work is partly financed by national funds through FCT – Foundation for Science and Technology, under grant agreement [2023.01101.BD] attributed to the third author.