Recurrent Convolutional Neural Networks help to predict location of Earthquakes [article]

Roman Kail, Alexey Zaytsev, Evgeny Burnaev
2020 arXiv   pre-print
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
more » ... s 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 · 10^3 correct predictions, while missing 2.09 · 10^3 earthquakes and making 192 · 10^3 false alarms. The baseline approach has similar ROC AUC 0.992, number of correct predictions 1.19 · 10^3, and missing 2.07 · 10^3 earthquakes, but significantly worse PR AUC 0.00911, and number of false alarms 1004 · 10^3.
arXiv:2004.09140v3 fatcat:grgkzpuyavcabaci37gavh5erq