How does a CNN mixed with LSTM methods compare with the individual one in predicting earthquakes?
I. Hashmi (TU Delft - Electrical Engineering, Mathematics and Computer Science)
Elvin Isufi – Mentor (TU Delft - Multimedia Computing)
Mohammad Sabbaqi – Mentor (TU Delft - Multimedia Computing)
Maosheng Yang – Mentor (TU Delft - Multimedia Computing)
DMJ Tax – Graduation committee member (TU Delft - Pattern Recognition and Bioinformatics)
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Abstract
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.