Papers › Recurrent Convolutional Neural Networks help to predict location of Earthquakes
Recurrent Convolutional Neural Networks help to predict location of Earthquakes
Roman Kail, Alexey Zaytsev, Evgeny Burnaev
We examine the applicability of modern neural network architectures to the midterm prediction of earthquakes. Our data-based classification model aims to predict if an earthquake with the magnitude above a threshold takes place at a given area of size 10 ×10 kilometers in $10$-$60$ days from a given moment. Our deep neural network model has a recurrent part (LSTM) that accounts for time dependencies between earthquakes and a convolutional part that accounts for spatial dependencies. Obtained results show that neural networks-based models beat baseline feature-based models that also account for spatio-temporal dependencies between different earthquakes. For historical data on Japan earthquakes our model predicts occurrence of an earthquake in $10$ to $60$ days from a given moment with magnitude M_c > 5 with quality metrics ROC AUC $0.975$ and PR AUC $0.0890$, making 1.18 ·10³ correct predictions, while missing 2.09 ·10³ earthquakes and making 192 ·10³ false alarms. The baseline approach has similar ROC AUC $0.992$, number of correct predictions 1.19 ·10³, and missing 2.07 ·10³ earthquakes, but significantly worse PR AUC $0.00911$, and number of false alarms 1004 ·10³.
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