Short-term Earthquake Prediction via Recurrent Neural Network Models

Short-term Earthquake Prediction via Recurrent Neural Network Models: Comparison among vanilla RNN, LSTM and Bi-LSTM

Du, XIANGYU (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

Earthquake prediction has raised many concerns nowadays, due to the massive loss caused by earthquakes, as well as the significance of accurate forecasting. Lots of trials have been investigated and experimented but few achieved satisfying results on short-term prediction (i.e., usually those earthquakes that will happen in three months). It is cardinal to detect strikes within a few minutes or hours in advance. In this paper, given thirty seconds of waveform signal before earthquakes happen, we compare the performances of three different recurrent neural networks, namely vanilla recurrent neural network (RNN), long short-term memory (LSTM) and bidirectional LSTM, on earthquake prediction. We choose recurrent neural networks because their inner structures take advantage of learning the temporal dependencies from time series sequence. Results show that LSTM has better performance predicting on unseen data than the other two networks.

Earthquake Prediction
Time Series Model
Recurrent Neural Network
Long Short-term Memory
Bidirectional Long Short-term Memory
Over-fitting
Grid Search

http://resolver.tudelft.nl/uuid:ccfcdc1e-bd7c-44cb-a834-5b6b651dc09e

Student theses

bachelor thesis

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