Automation of Spatiotemporal Analysis of Seismic Migration Using Computational Algorithms

This project addresses the automation of spatiotemporal seismic migration analysis in the Pacific Ring of Fire, a region responsible for 90% of global earthquakes. Despite the importance of identifying migratory chains for seismic prediction, current methods rely on limited manual analysis. The research proposes an algorithm based on the Vikulin method, applied to historical seismic catalogs from IRIS (1970-2025), to process large volumes of data systematically and scalably. The methodological approach uses data mining, machine learning, and geospatial analysis techniques in Python to calculate critical parameters such as migration velocity, duration, and distance traveled. By eliminating human error and enabling massive processing, the system offers an innovative tool for applied geophysics. Expected results include the identification of precursor patterns of seismic activity, which will strengthen early warning strategies and risk management at an international level. This work not only contributes to scientific advancement in seismology but also promotes the resilience of communities located in high tectonic risk zones through the use of advanced computational technologies.<br/><br/><b>Goal</b>: <br/>Develop an automated system for the spatiotemporal analysis of seismic migration using computational algorithms. The goal is to identify seismic event propagation patterns to contribute to the understanding of tectonic dynamics and seismic risk mitigation.<br/><br/><b>Research lines</b>: <br/>Modeling and simulation applied to industry