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.
I. N. Athanasiadis, Realization and comparison of P- and S- seismic phases detection algorithms using neural networks, 2000.
You might also enjoy (View all publications)
- Enabling reusability of plant phenomic datasets with MIAPPE 1.1
- Defining and classifying infrastructural contestation: Towards a synergy between anthropology and data science
- Investigation of common big data analytics and decision-making requirements across diverse precision agriculture and livestock farming use cases