Print Email Facebook Twitter Impact of seismic wave length to detect high-magnitude earthquakes via deep learning Title Impact of seismic wave length to detect high-magnitude earthquakes via deep learning Author Georgiev, Gancho (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Sabbaqi, M. (mentor) Isufi, E. (mentor) Brinkman, W.P. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2022-06-22 Abstract Earthquakes are one of the most destructive natural phenomena, both in terms of human lives, and property damage. Although they are treated as a random phenomenon, the ability to predict them, even few seconds before they occur, could be of great benefit to society. Lots of research has been done on this topic but without any significant results. With the increase of seismic wave measurements data and since in recent years deep learning has solved many difficult problems, this paper aims to answer the impact of the seismic wave length in detecting high-magnitude earthquakes via Long Short-Term Memory (LSTM) neural network. Although the performance of the model was unsatisfactory, given the complex task of predicting earthquakes, as well as the resulting metrics not indicating any significant data in order to extrapolate a certain conclusion, it is worth further researching a duration of seismic waveform recordings of length 30 seconds, with sampling rate between 10 and 20 HZ, as these seismic waves seem to perform relatively best in our research. Subject Earthquake PredictionLong Short-term MemoryNeural network To reference this document use: http://resolver.tudelft.nl/uuid:a3305121-f074-4f8b-a18d-e512c484733a Part of collection Student theses Document type bachelor thesis Rights © 2022 Gancho Georgiev Files PDF BSc_Research_Paper_Georgiev_G.pdf 1.26 MB Close viewer /islandora/object/uuid:a3305121-f074-4f8b-a18d-e512c484733a/datastream/OBJ/view