Short-term Earthquake Prediction with Deep Neural Networks

Short-term Earthquake Prediction with Deep Neural Networks: Finding the optimal time prior to earthquake strikes to use in predictions

van den Belt, Glenn (TU Delft Electrical Engineering, Mathematics and Computer Science)

Sabbaqi, M. (mentor)
Isufi, E. (graduation committee)

Delft University of Technology

Applied Mathematics

2022-08-01

Earthquakes can have tremendous effects. They can result in casualties, massive damage, and hurt the economy. Therefore, one would like to predict earthquakes as early as possible and with the highest accuracy possible. This paper contains the proposal for the optimal prediction-time, which is the time between the execution of a prediction and the actual earthquake strike, for deep learning models. Only short-term predictions and high-magnitude earthquakes are considered. A prediction means to define whether an earthquake happens or does not happen in an upcoming amount of seconds. A short-term prediction means a forecast to the extent of seconds. A high-magnitude earthquake means an earthquake with a magnitude of 2.5 or higher. This research uses the Long Short Term Memory deep learning model to test the optimal prediction-time value for earthquake predictions.
The optimal value for the prediction-time is found by testing the model with different values for the prediction-time and concluding when the model performs best. For prediction-times from moments before the strike until 40 seconds, the model is performing worse compared to higher prediction-times. The model's performance peaks at a prediction-time of 70. When increasing further than 70, the performance decreases until a prediction-time of one hundred. When rising even further, the performance is stabilising. Thus, for predictions with the highest performance, one should use a prediction-time of 70 seconds.

Earthquake
Deep learning
Prediction
Machine learning
LSTM
long short term memory

http://resolver.tudelft.nl/uuid:902c0256-0e4e-410d-8f22-04bf526484d6

Student theses

bachelor thesis

© 2022 Glenn van den Belt