Identification of earthquakes and microtremors based on a combined model of generative adversarial network and random forest

PreviousNext No AccessSEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021Identification of earthquakes and microtremors based on a combined model of generative adversarial network and random forestAuthors: Liu HeyiSong JindongWei YongxiangLI ShanyouLiu HeyiKey Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake AdministrationKey Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency ManagementSearch for more papers by this author, Song JindongKey Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake AdministrationKey Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency ManagementSearch for more papers by this author, Wei YongxiangEarthquake Administration of Fujian ProvinceSearch for more papers by this author, and LI ShanyouKey Laboratory of Earthquake Engineering and Engineering Vibration, Institute of Engineering Mechanics, China Earthquake AdministrationKey Laboratory of Earthquake Disaster Mitigation, Ministry of Emergency ManagementSearch for more papers by this authorhttps://doi.org/10.1190/iwmg2021-09.1 SectionsAboutPDF/ePub ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InRedditEmail Abstract Seismic event identification is one of the basic tasks of earthquake monitoring. Especially with the construction of seismic stations, the collection of massive seismic data, and the development of earthquake early warning (EEW), how to distinguish between earthquakes and noise from continuous waveforms become increasingly vital. To accurately identify seismic events, a combined model based on a generative adversarial network (GAN) and a Random Forest (RF) is provided to distinguish between earthquakes and microtremors. We first train a GAN with 52537 strong ground motion records, and use the modified discriminator to extract waveform features, and then use a RF to discriminate seismic events in the testing set which has 5378 data of earthquakes and microtremors, thereby transforming seismic events identification into a classification problem. The results show that, the classification accuracy of the combined model for classifying seismic events and microtremor can reach more than 99%. It illustrates that the proposed model can accurately identify seismic events and has application prospects in earthquake monitoring. Keywords: machine learning, algorithm, monitoring, processing, P-wavePermalink: https://doi.org/10.1190/iwmg2021-09.1FiguresReferencesRelatedDetails SEG 2021 Workshop: 4th International Workshop on Mathematical Geophysics: Traditional & Learning, Virtual, 17–19 December 2021ISSN (online):2159-6832Copyright: 2022 Pages: 193 publication data© 2022 Published in electronic format with permission by the Society of Exploration GeophysicistsPublisher:Society of Exploration Geophysicists HistoryPublished: 24 Feb 2022 CITATION INFORMATION Liu Heyi, Song Jindong, Wei Yongxiang, and LI Shanyou, (2022), "Identification of earthquakes and microtremors based on a combined model of generative adversarial network and random forest," SEG Global Meeting Abstracts : 33-36. https://doi.org/10.1190/iwmg2021-09.1 Plain-Language Summary Keywordsmachine learningalgorithmmonitoringprocessingP-wavePDF DownloadLoading ...