TY - GEN
T1 - Convolutional Recurrent Neural Networks for Earthquake Epicentral Distance Estimation Using Single-Channel Seismic Waveform
AU - Kim, Gwantae
AU - Ku, Bonhwa
AU - Li, Yuanming
AU - Min, Jeongki
AU - Lee, Jimin
AU - Ko, Hanseok
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/9/26
Y1 - 2020/9/26
N2 - This paper proposes a deep learning method for epicentral distance estimation using a single-channel seismic waveform. The model is based on a convolutional recurrent neural network structure to extract spatial and temporal features. Since the proposed model needs only single-channel data, it can also perform the distance estimation even when some channels of the sensor are adversely disabled. To evaluate our approach, we conduct distance estimation experiments with the Korean peninsula earthquake database from 2016 to 2018, which include microearthquakes and distant earthquakes. The epicentral distance estimation by the proposed method show an absolute mean error of 0.50 km with 9.16km standard deviation of error distribution, which shows the best estimation result among the competing model structures. The promising result indicates that the proposed approach can be deployed for epicentral localization task as part of realizing a robust earthquake monitoring system.
AB - This paper proposes a deep learning method for epicentral distance estimation using a single-channel seismic waveform. The model is based on a convolutional recurrent neural network structure to extract spatial and temporal features. Since the proposed model needs only single-channel data, it can also perform the distance estimation even when some channels of the sensor are adversely disabled. To evaluate our approach, we conduct distance estimation experiments with the Korean peninsula earthquake database from 2016 to 2018, which include microearthquakes and distant earthquakes. The epicentral distance estimation by the proposed method show an absolute mean error of 0.50 km with 9.16km standard deviation of error distribution, which shows the best estimation result among the competing model structures. The promising result indicates that the proposed approach can be deployed for epicentral localization task as part of realizing a robust earthquake monitoring system.
KW - convolutional recurrent neural networks
KW - deep learning
KW - epicentral distance estimation
KW - single-channel waveform
UR - http://www.scopus.com/inward/record.url?scp=85101990635&partnerID=8YFLogxK
U2 - 10.1109/IGARSS39084.2020.9323360
DO - 10.1109/IGARSS39084.2020.9323360
M3 - Conference contribution
AN - SCOPUS:85101990635
T3 - International Geoscience and Remote Sensing Symposium (IGARSS)
SP - 6619
EP - 6622
BT - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2020
Y2 - 26 September 2020 through 2 October 2020
ER -