Realization and comparison of P- and S- seismic phases detection algorithms using neural networks
The diploma thesis studies the implementation and comparison of algorithms for P- and S- seismic phases detection, using neural networks. The thesis is structured in 6 chapters: Chapter 1 presents the principles of seismology, earthquake generation, seismic waves and their recording and seismic phases P and S. Chapter 2 summarizes the state-of-the-art in seismic phases detection using polarization, energy and autoregressive models. Neural networks are detailed in Chapter 3, in terms of principles, architectures, use and advantages. Chapters 4 and 5 present a framework that utilizes neural networks for seismic phase detection (P and S accordingly). The last chapter 6 presents the conclusions of the thesis and indicate future research work. In the appendices, all experimental results are presented in tables along with various statistics.
Download full text in pdf format
I. N. Athanasiadis, Realization and comparison of P- and S- seismic phases detection algorithms using neural networks, MSc Thesis, pg. 98, 2000, Aristotle University of Thessaloniki.
You might also enjoy (View all publications)
- Learning long-term crop management strategies with CyclesGym
- Rapid turnover of sensor data to genetic evaluation for dairy cows in the cloud
- Mixing process-based and data-driven approaches in yield prediction