Backbone ground motion model through simulated records and XGBoost machine learning algorithm: An application for the Azores plateau (Portugal)

Browse:
/
/
/
Backbone ground motion model through simulated records and XGBoost machine learning algorithm: An application for the Azores plateau (Portugal)
Journal
Earthquake Engineering and Structural Dynamics
Title
Backbone ground motion model through simulated records and XGBoost machine learning algorithm: An application for the Azores plateau (Portugal)
Authors
Shaghayegh Karimzadeh, Amirhossein Mohammadi, Usman Salahuddin, Alexandra Carvalho, Paulo B. Lourenço
Date
December 4, 2023
ABSTRACT

Azores Islands are seismically active due to the tectonic structure of the region. Since the 15th century, they have been periodically shaken by approximately 33 moderate to strong earthquakes, with the most recent one in 1998 (Mw = 6.2). Nonetheless, due to insufficient instrumental seismic data, the region lacks a uniform database of past real records. Ground motion simulation techniques provide alternative region-specific time series of prospective events for locations with limited seismic networks or regions with a seismic gap of catastrophic earthquake events. This research establishes a local ground motion model (GMM) for the Azores plateau (Portugal) by simulating region-specific records for constructing a homogeneous dataset. Simulations are accomplished in bedrock using the stochastic finite-fault approach by employing validated input-model parameters. The simulation results undergo validation against the 1998 Faial event and comparison with empirical models for volcanic and Pan-European datasets. A probabilistic numerical technique, namely the Monte-Carlo simulation, is employed to estimate the outcome of uncertainty associated with these parameters. The results of the simulations are post-processed to predict the peak ground motion parameters in addition to spectral ordinates. This study uses XGBoost to circumvent the difficulties inherent to linear regression-based models in establishing the form of equations and coefficients. The input parameters for prediction are moment magnitude (Mw), Joyner and Boore distance (RJB), and focal depth (FD). The quantification of GMM uncertainty is accomplished by analyzing the residuals, providing insight into inter- and intra-event uncertainties. The outcomes demonstrate the effectiveness of the suggested model in simulating physical phenomena.

Latest Publications

Backbone
Shaghayegh Karimzadeh, Amirhossein Mohammadi, Usman Salahuddin, Alexandra Carvalho, Paulo B. Lourenço
Journal Paper
2023
GMM_Turkey
Shaghayegh Karimzadeh, Amirhossein Mohammadi, Sayed Mohammad Sajad Hussaini, Daniel Caicedo, Aysegul Askan, and Paulo B. Lourenço
Journal Paper
2023
eesd-sjh
Shaghayegh Karimzadeh, Marco F. Funari, Simon Szabó, S. M. Sajad Hussaini, Sanaz Rezaeian, Paulo B. Lourenço
Journal Paper
2023
Figure news S4H
Carla Colombo, Georgios Vlachakis, Christiam C. Angel, Anastasios I. Giouvanidis, Nathanaёl Savalle, Nuno Mendes, Paulo B. Lourenço
Conference Paper
2023