Seismic Risk Assessment Model Based on the Migration of Seismic Activity in Different Seismic Zones of the World (Phase 2)

This project undertakes an exhaustive analysis of seismic activity within the global Fire Belts, aiming to establish correlations with geophysical and spatial phenomena, such as solar activity and changes in the Earth's magnetic field. The core problem addressed is the complexity of earthquake prediction, despite known correlations between seismic wave energy and velocity, and the potential influence of space weather. The proposed solution involves applying advanced computational methodologies, including Big Data and Machine Learning, to process and analyze large volumes of seismic data. The methodology mandates rigorous data collection from key sources like IRIS, NOAA, NEIC, and USGS. The ultimate goal is to develop a mathematical earthquake prediction model, validating results by analyzing Earth's crustal layers and tectonic plate movement, in order to identify vulnerable zones and advance toward potential seismic forecasting.<br/><br/><b>Goal</b>: <br/>To analyze the seismic activity of the global Fire Belts and correlate it with physical and mathematical principles by utilizing global seismic databases, supported by Big Data tools, Machine Learning, and computational methodologies.<br/><br/><b>Research lines</b>: <br/>Data science and simulation