This project focused on the processing and analysis of seismic data as part of my research at Freie Universität Berlin. The objective was to extract quantitative information from seismic records and use it to better understand earthquake activity and its characteristics. I worked with seismic datasets containing signals generated by earthquakes and other sources of ground motion.

Python was used to organize, process, visualize, and analyze the data systematically. Numerical calculations were implemented to extract relevant parameters and compare seismic events. Particular attention was given to distinguishing meaningful seismic signals from background noise and understanding variations in the recorded waveforms. Visualization of seismic traces helped identify differences in signal amplitude, timing, duration, and overall waveform characteristics.

The processed information was used to investigate temporal and spatial patterns within the seismic dataset. Statistical analysis provided an additional way to compare events and identify trends that were not immediately visible in individual seismic records. Working with these datasets required careful data handling because processing choices can strongly influence the interpretation of seismic signals. The project strengthened my ability to work with large scientific datasets and translate raw measurements into interpretable physical information. It also provided an important foundation for my later research into the relationship between earthquakes, surface processes, and boulder movement in tectonically active landscapes.