TY - JOUR

T1 - Recurrent Convolutional Neural Networks Help to Predict Location of Earthquakes

AU - Kail, Roman

AU - Burnaev, Evgeny

AU - Zaytsev, Alexey

N1 - Funding Information:
This work was supported by the Ministry of Science and Higher Education under Grant 075-10-2021-068.
Publisher Copyright:
© 2004-2012 IEEE.

PY - 2022

Y1 - 2022

N2 - We develop a neural network (NN) architecture aimed at the midterm prediction of earthquakes. Our data-based model aims to predict if an earthquake with a magnitude above a threshold takes place at a given small area of size 10 km $\times $ 10 km in a midterm range of 10-50 days from a given moment. Our deep NN model has a recurrent part long short term memory (LSTM) that accounts for time dependencies between earthquakes and a convolutional part that accounts for spatial dependencies. Obtained results show that NNs-based models beat baseline feature-based models that also account for spatio-temporal dependencies between different earthquakes. Moreover, each part of our network is essential for its quality. For historical data on Japan earthquakes, our model predicts the occurrence of an earthquake in a period of 10 to 50 days from a given moment with magnitude $M_{c} > 5$ missing $2.09 \cdot 10^{3}$ earthquakes out of $3.11 \cdot 10^{3}$ and making $192 \cdot 10^{3}$ false alarms. The baseline approach misses $2.07 \cdot 10^{3}$ earthquakes but with a significantly higher number of false alarms $1004 \cdot 10^{3}$.

AB - We develop a neural network (NN) architecture aimed at the midterm prediction of earthquakes. Our data-based model aims to predict if an earthquake with a magnitude above a threshold takes place at a given small area of size 10 km $\times $ 10 km in a midterm range of 10-50 days from a given moment. Our deep NN model has a recurrent part long short term memory (LSTM) that accounts for time dependencies between earthquakes and a convolutional part that accounts for spatial dependencies. Obtained results show that NNs-based models beat baseline feature-based models that also account for spatio-temporal dependencies between different earthquakes. Moreover, each part of our network is essential for its quality. For historical data on Japan earthquakes, our model predicts the occurrence of an earthquake in a period of 10 to 50 days from a given moment with magnitude $M_{c} > 5$ missing $2.09 \cdot 10^{3}$ earthquakes out of $3.11 \cdot 10^{3}$ and making $192 \cdot 10^{3}$ false alarms. The baseline approach misses $2.07 \cdot 10^{3}$ earthquakes but with a significantly higher number of false alarms $1004 \cdot 10^{3}$.

KW - Earthquakes

KW - machine learning (ML)

KW - neural networks (NNs)

KW - prediction methods

KW - recurrent neural networks (RNNs)

UR - http://www.scopus.com/inward/record.url?scp=85114748898&partnerID=8YFLogxK

U2 - 10.1109/LGRS.2021.3107998

DO - 10.1109/LGRS.2021.3107998

M3 - Article

AN - SCOPUS:85114748898

VL - 19

JO - IEEE Geoscience and Remote Sensing Letters

JF - IEEE Geoscience and Remote Sensing Letters

SN - 1545-598X

ER -