ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano
With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be consid...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
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| Online Access: | https://ieeexplore.ieee.org/document/9477001/ |
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| _version_ | 1846127423810174976 |
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| author | Felipe Grijalva Washington Ramos Noel Perez Diego Benitez Roman Lara Mario Ruiz |
| author_facet | Felipe Grijalva Washington Ramos Noel Perez Diego Benitez Roman Lara Mario Ruiz |
| author_sort | Felipe Grijalva |
| collection | DOAJ |
| description | With the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by <italic>in situ</italic> monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance. |
| format | Article |
| id | doaj-art-00cd7fb105a04eacac5402eb73f81607 |
| institution | Kabale University |
| issn | 1939-1404 2151-1535 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
| spelling | doaj-art-00cd7fb105a04eacac5402eb73f816072024-12-12T00:00:08ZengIEEEIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing1939-14042151-15352021-01-01147111712010.1109/JSTARS.2021.30952709477001ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi VolcanoFelipe Grijalva0https://orcid.org/0000-0001-6162-3429Washington Ramos1https://orcid.org/0000-0002-3311-7446Noel Perez2https://orcid.org/0000-0003-3166-745XDiego Benitez3https://orcid.org/0000-0001-6219-067XRoman Lara4https://orcid.org/0000-0001-8848-9928Mario Ruiz5https://orcid.org/0000-0002-0872-6573Faculty of Engineering and Applied Sciences (FICA), Telecommunications Engineering, Universidad de Las Américas (UDLA), Quito, EcuadorDepartamento de Electrónica, Telecomunicaciones y Redes de Información (DETRI), Escuela Politécnica Nacional, Quito, EcuadorColegio de Ciencias e Ingenierías “El Politécnico,”, Universidad San Francisco de Quito (USFQ), Quito, EcuadorColegio de Ciencias e Ingenierías “El Politécnico,”, Universidad San Francisco de Quito (USFQ), Quito, EcuadorGrupo de Investigación en Sistemas Inteligentes (WiCOM-Energy), Sangolquí, EcuadorInstituto Geofísico, Escuela Politécnica Nacional, Quito, EcuadorWith the growing ability to collect large volumes of volcano seismic data, the detection and labeling process of these records is increasingly challenging. Clearly, analyzing all available data through manual inspection is no longer a viable option. Supervised machine learning models might be considered to automatize the analysis of data acquired by <italic>in situ</italic> monitoring stations. However, the direct application of such algorithms is defiant, given the high complexity of waveforms and the scarce and often imbalanced amount of labeled data. In light of this and motivated by the wide success that generative adversarial networks (GANs) have seen at generating images, we present ESeismic-GAN, a GAN model to generate the magnitude frequency response of volcanic events. Our experiments demonstrate that ESeismic-GAN learns to generate the frequency components that characterize long-period and volcano-tectonic events from Cotopaxi volcano. We evaluate the performance of ESeismic-GAN during the training stage using Fréchet distance, and, later on, we reconstruct the signals into time-domain to be finally evaluated with Frechet inception distance.https://ieeexplore.ieee.org/document/9477001/Adversarial learningCotopaxigenerative adversarial networks (GANs)generative modelseismicvolcano |
| spellingShingle | Felipe Grijalva Washington Ramos Noel Perez Diego Benitez Roman Lara Mario Ruiz ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Adversarial learning Cotopaxi generative adversarial networks (GANs) generative model seismic volcano |
| title | ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano |
| title_full | ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano |
| title_fullStr | ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano |
| title_full_unstemmed | ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano |
| title_short | ESeismic-GAN: A Generative Model for Seismic Events From Cotopaxi Volcano |
| title_sort | eseismic gan a generative model for seismic events from cotopaxi volcano |
| topic | Adversarial learning Cotopaxi generative adversarial networks (GANs) generative model seismic volcano |
| url | https://ieeexplore.ieee.org/document/9477001/ |
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