TY - GEN
T1 - ConvLSTM Neural Networks for seismic event prediction in Chile
AU - Fuentes, Alex Gonzalez
AU - Nicolis, Orietta
AU - Peralta, Billy
AU - Chiodi, Marcello
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/8/5
Y1 - 2021/8/5
N2 - Predicting seismic risk is a challenging task in order to avoid catastrophic effects. In this work, two models based on Convolutional Network (CNN) and Long Short Term Memory (LSTM) networks are proposed to predict the seismic risk in Chile. In particular, a ConvLSTM and a Multi-column ConvLSTM network are used for the prediction of the average number of seismic events greater than 2,8 magnitude on the Richter scale, in the Chilean regions of Coquimbo and Araucania between the years 2010 and 2017. For this model, the values of the intensity function estimated through an ETAS model and the accumulated displacement prior to a the seismic events are used as inputs. In particular, given the spatial and temporal characteristics of the seismic data, matrices of size 20x20 of the last 20 days are considered to predict the average number of seismic events of the next day in a given area. From the results obtained, the Multi-column ConvLSTM network achieved a coefficient of determination of 0,804 and a lower MSE than other networks.
AB - Predicting seismic risk is a challenging task in order to avoid catastrophic effects. In this work, two models based on Convolutional Network (CNN) and Long Short Term Memory (LSTM) networks are proposed to predict the seismic risk in Chile. In particular, a ConvLSTM and a Multi-column ConvLSTM network are used for the prediction of the average number of seismic events greater than 2,8 magnitude on the Richter scale, in the Chilean regions of Coquimbo and Araucania between the years 2010 and 2017. For this model, the values of the intensity function estimated through an ETAS model and the accumulated displacement prior to a the seismic events are used as inputs. In particular, given the spatial and temporal characteristics of the seismic data, matrices of size 20x20 of the last 20 days are considered to predict the average number of seismic events of the next day in a given area. From the results obtained, the Multi-column ConvLSTM network achieved a coefficient of determination of 0,804 and a lower MSE than other networks.
KW - Deep learning
KW - ETAS
KW - prediction
KW - seismic risk
UR - http://www.scopus.com/inward/record.url?scp=85116274852&partnerID=8YFLogxK
U2 - 10.1109/INTERCON52678.2021.9532946
DO - 10.1109/INTERCON52678.2021.9532946
M3 - Conference contribution
AN - SCOPUS:85116274852
T3 - Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
BT - Proceedings of the 2021 IEEE 28th International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 28th IEEE International Conference on Electronics, Electrical Engineering and Computing, INTERCON 2021
Y2 - 5 August 2021 through 7 August 2021
ER -