DOCTORADO EN INGENIERÍA - ÉNFASIS EN CIENCIAS DE LA COMPUTACIÓN · 2025

Artificial intelligence and complex network- based model for seismicity analysis in the colombian region

Research about Earthquake Networks has been carried on for many years, and they have been proved as a tool to analyze the seismicity. However, there has been no comprehensive integration of this representation with artificial intelligence methods to model the dynamics of seismicity. This thesis presents a novel approach to find how Earthquake Networks, supported by Machine Learning methods, can be used to look into the dynamics of the seismicity in the Colombian region. This thesis shows how can Earthquake Networks be constructed to represent the seismicity in the region of study. The information of such networks has been analyzed, contrasting their characteristics with the variations of the seismicity in time, in order to find an appropriate way of creating the Earthquake Networks. Also, data analysis of a seismic catalog is performed to develop an annotation schema and a pipeline for the application of Machine Learning algorithms with Earthquake Networks information. The outputs of the Machine Learning models trained have been analyzed using different metrics, and some models appeared fit to forecast some aspects of the seismicity such as the seismic energy released by the expected events, or the moment of occurrence of the next event. The findings indicate that earthquake networks, combined with machine learning techniques, can provide valuable insights into seismic behavior, and may serve as a predictive tool for seismic monitoring. These models can potentially be integrated into real-time systems for seismic observatories, offering forecasting and alerts to users of interest.

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