TY - GEN
T1 - Deep-Learning for Volcanic Seismic Events Classification
AU - Salazar, Aaron
AU - Arroyo, Rodrigo
AU - Perez, Noel
AU - Benitez, Diego
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/8/7
Y1 - 2020/8/7
N2 - In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The three deep recurrent neural network-based models reached the worst results due to the overfitting produced by the small number of samples in the training sets. The DCNN1 overcame the remaining models by touching an area under the curve of the receiver operating characteristic and accuracy scores of 0.98 and 95%, respectively. Although these values were not the highest values per metric, they did not represent statistical differences against other results obtained by more algorithmically complex models. The proposed DCNN1 model showed similar or superior performance when compared to the majority of the state of the art methods in terms of the accuracy metric. Therefore it can be considered a successful scheme to classify LP and VT seismic events based on their spectrogram images.
AB - In this work, we proposed a new method to classify long-period and volcano-tectonic spectrogram images using six different deep learning architectures. The developed method used three deep convolutional neural networks named: DCNN1, DCNN2, and DCNN3. Also, three deep convolutional neural networks combined with deep recurrent neural networks named DCNN-RNN1, DCNN-RNN2, and DCNN-RNN3 to maximize the area under the curve of the receiver operating characteristic scores on a dataset of volcano seismic spectrogram images. The three deep recurrent neural network-based models reached the worst results due to the overfitting produced by the small number of samples in the training sets. The DCNN1 overcame the remaining models by touching an area under the curve of the receiver operating characteristic and accuracy scores of 0.98 and 95%, respectively. Although these values were not the highest values per metric, they did not represent statistical differences against other results obtained by more algorithmically complex models. The proposed DCNN1 model showed similar or superior performance when compared to the majority of the state of the art methods in terms of the accuracy metric. Therefore it can be considered a successful scheme to classify LP and VT seismic events based on their spectrogram images.
KW - artificial intelligence
KW - deep-learning models
KW - spectrogram images
KW - volcanic seismic event classification
UR - https://www.scopus.com/pages/publications/85097532493
U2 - 10.1109/ColCACI50549.2020.9247848
DO - 10.1109/ColCACI50549.2020.9247848
M3 - Contribución a la conferencia
AN - SCOPUS:85097532493
T3 - 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
BT - 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020 - Proceedings
A2 - Orjuela-Canon, Alvaro David
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE Colombian Conference on Applications of Computational Intelligence, ColCACI 2020
Y2 - 7 August 2020 through 9 August 2020
ER -