Seismic phase recognition model with low SNR based on U-net
Aiming at the problem of low recognition accuracy and high missed detection rate of seismic phase recognition of low signal-to-noise ratio seismic signals, a new seismic phase recognition model UBAN (U-net-Bidirectional Gated Recurrent Unit-Attention Network) is designed based on U-net neural networ...
Saved in:
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2025-12-01
|
| Series: | Geomatics, Natural Hazards & Risk |
| Subjects: | |
| Online Access: | https://www.tandfonline.com/doi/10.1080/19475705.2025.2481992 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850205347179921408 |
|---|---|
| author | Jianxian Cai Zhongjie Sun Mengying Zhang Fenfen Yan Li Wang Ling Li |
| author_facet | Jianxian Cai Zhongjie Sun Mengying Zhang Fenfen Yan Li Wang Ling Li |
| author_sort | Jianxian Cai |
| collection | DOAJ |
| description | Aiming at the problem of low recognition accuracy and high missed detection rate of seismic phase recognition of low signal-to-noise ratio seismic signals, a new seismic phase recognition model UBAN (U-net-Bidirectional Gated Recurrent Unit-Attention Network) is designed based on U-net neural network framework, combined with Bi-GRU bidirectional gated recurrent unit and Attention attention mechanism. In this model, Bi-GRU bidirectional gated recurrent unit and Attention attention mechanism are added between the U-net coding layer and the decoding layer. Bi-GRU is suitable for processing long time series signals,and attention mechanism is used to pay attention to the time series characteristics of seismic phases, ignoring the advantages of useless features such as noise and peaks, improving the network ‘s ability to perceive the arrival time characteristics and improving the recognition accuracy. The seismic data of Stanford University are used to train and test the designed UBAN phase recognition model. The experimental results show that the UBAN phase recognition model shows good performance in the phase recognition of low signal-to-noise ratio seismic signals, which provides a new idea for the research of low signal-to-noise ratio seismic signal phase recognition method. |
| format | Article |
| id | doaj-art-c5ff67eceb7b4556be1db91c0e6780ec |
| institution | OA Journals |
| issn | 1947-5705 1947-5713 |
| language | English |
| publishDate | 2025-12-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Geomatics, Natural Hazards & Risk |
| spelling | doaj-art-c5ff67eceb7b4556be1db91c0e6780ec2025-08-20T02:11:08ZengTaylor & Francis GroupGeomatics, Natural Hazards & Risk1947-57051947-57132025-12-0116110.1080/19475705.2025.2481992Seismic phase recognition model with low SNR based on U-netJianxian Cai0Zhongjie Sun1Mengying Zhang2Fenfen Yan3Li Wang4Ling Li5Institute of Disaster Prevention, Sanhe, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, Hebei, ChinaInstitute of Disaster Prevention, Sanhe, Hebei, ChinaAiming at the problem of low recognition accuracy and high missed detection rate of seismic phase recognition of low signal-to-noise ratio seismic signals, a new seismic phase recognition model UBAN (U-net-Bidirectional Gated Recurrent Unit-Attention Network) is designed based on U-net neural network framework, combined with Bi-GRU bidirectional gated recurrent unit and Attention attention mechanism. In this model, Bi-GRU bidirectional gated recurrent unit and Attention attention mechanism are added between the U-net coding layer and the decoding layer. Bi-GRU is suitable for processing long time series signals,and attention mechanism is used to pay attention to the time series characteristics of seismic phases, ignoring the advantages of useless features such as noise and peaks, improving the network ‘s ability to perceive the arrival time characteristics and improving the recognition accuracy. The seismic data of Stanford University are used to train and test the designed UBAN phase recognition model. The experimental results show that the UBAN phase recognition model shows good performance in the phase recognition of low signal-to-noise ratio seismic signals, which provides a new idea for the research of low signal-to-noise ratio seismic signal phase recognition method.https://www.tandfonline.com/doi/10.1080/19475705.2025.2481992Seismic phase recognitionU-net (U-shaped neural network)Bi-GRU (bidirectional-gated recurrent unit)attention mechanismseismic phase with low SNR |
| spellingShingle | Jianxian Cai Zhongjie Sun Mengying Zhang Fenfen Yan Li Wang Ling Li Seismic phase recognition model with low SNR based on U-net Geomatics, Natural Hazards & Risk Seismic phase recognition U-net (U-shaped neural network) Bi-GRU (bidirectional-gated recurrent unit) attention mechanism seismic phase with low SNR |
| title | Seismic phase recognition model with low SNR based on U-net |
| title_full | Seismic phase recognition model with low SNR based on U-net |
| title_fullStr | Seismic phase recognition model with low SNR based on U-net |
| title_full_unstemmed | Seismic phase recognition model with low SNR based on U-net |
| title_short | Seismic phase recognition model with low SNR based on U-net |
| title_sort | seismic phase recognition model with low snr based on u net |
| topic | Seismic phase recognition U-net (U-shaped neural network) Bi-GRU (bidirectional-gated recurrent unit) attention mechanism seismic phase with low SNR |
| url | https://www.tandfonline.com/doi/10.1080/19475705.2025.2481992 |
| work_keys_str_mv | AT jianxiancai seismicphaserecognitionmodelwithlowsnrbasedonunet AT zhongjiesun seismicphaserecognitionmodelwithlowsnrbasedonunet AT mengyingzhang seismicphaserecognitionmodelwithlowsnrbasedonunet AT fenfenyan seismicphaserecognitionmodelwithlowsnrbasedonunet AT liwang seismicphaserecognitionmodelwithlowsnrbasedonunet AT lingli seismicphaserecognitionmodelwithlowsnrbasedonunet |