Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction

Comparing multichannel mixed CNN-RNN to individual models for earthquake prediction

Houbaer, Maikel (TU Delft Electrical Engineering, Mathematics and Computer Science)

Isufi, E. (mentor)
Yang, M. (mentor)
Sabbaqi, M. (mentor)
Tax, D.M.J. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-01-28

Earthquakes can do great harm to the environment and people's daily lives. Being able to predict an earthquake moments before it happens could therefore reduce harm and save human lives. Traditional methods have not been successful yet, but with the rise of techniques focused on deep learning, there is a growing interest to apply them to the field of earthquakes. The placement of stations measuring seismic waves at various locations across regions has also greatly contributed to the possibility of applying data-driven techniques to the problem. A neural network that has been previously successful in the prediction of epileptic seizures - is a CNN mixed with RNN methods. In this paper, we validate the use of this model in predicting earthquakes and compare its performance to individual models. We do this based on seismic measurements before the earthquakes of different stations across New Zealand. The results suggest that our method is not capable of predicting earthquakes with higher accuracy than random guessing.

Earthquake Prediction
Neural Network
CNN
RNN

http://resolver.tudelft.nl/uuid:0f80cf6e-1cb5-4296-81f3-43374afa49a7

Student theses

bachelor thesis

© 2022 Maikel Houbaer