How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?

Hashmi, Irtaza (TU Delft Electrical Engineering, Mathematics and Computer Science)

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

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2022-01-23

Earthquakes are one of the most dangerous natural disasters that occur worldwide. Predicting them is one of the unsolved problems in the field of science. In the past decade, there has been an increase in seismic monitoring stations worldwide, which has allowed us to design and implement data-driven and deep learning solutions. In this paper, we will investigate how CNN mixed with LSTM methods compare to the individual ones in predicting earthquakes given 30 seconds of seismic data before an earthquake occurs, also known as precursor data. Preliminary results show that a CNN mixed with LSTM has the best training accuracy while an individual LSTM performs best on unseen data.

Deep neural network
convolutional neural network
long short-term memory
earthquakes prediction
seismic data
precursor data

http://resolver.tudelft.nl/uuid:c6c6a920-3a1f-477f-9ef5-5cb370a97924

Student theses

bachelor thesis

© 2022 Irtaza Hashmi