Seismic first break picking based on multi-task learning
IntroductionSeismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot topic in recent years, and multi-task learning has also demonstrated excellent data representation ca...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Earth Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/feart.2025.1601134/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849318287738929152 |
|---|---|
| author | Zhongpo Zhang Jing Yang |
| author_facet | Zhongpo Zhang Jing Yang |
| author_sort | Zhongpo Zhang |
| collection | DOAJ |
| description | IntroductionSeismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot topic in recent years, and multi-task learning has also demonstrated excellent data representation capabilities in several areas.MethodsTo improve accuracy, we combine the FB picking task with the seismic data reconstruction task, and propose an enhanced FB picking training method based on multi-task learning network. Specifically, we use two decoding branches of the same size in the network, which are the ISS decoding branch for the FB picking task, and the seismic feature learning decoding branch for the reconstruction task. The introduction of the seismic feature learning decoding branch will further help the network encoder to extract seismic effective features more efficiently, which will improve the accuracy of the ISS decoding branch, and ultimately improve the accuracy of the FB picking. During the training process, we use different loss functions for different decoding branches, and guide the network fitting through joint loss. In addition, we randomly add noise as well as random elimination to the seismic data to simulate the low SNR trace sets and bad traces that may exist in seismic data acquisition, and discuss the impact of different cases on the training results.Results and discussionThe experimental results show that this method achieves more accurate FB picking results than the existing single-branch ISS methods, with an average picking error as low as 3.08 ms in the field data, and the percentage of traces with a picking error higher than 15 samples is as low as 0.03%, which is far superior to the network methods such as UNet, STUNet, SegNet, and Res-Unet, and effectively realizes the overall high-quality FB picking. |
| format | Article |
| id | doaj-art-b86189cc494d4db6889bdbf5d4b52665 |
| institution | Kabale University |
| issn | 2296-6463 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Earth Science |
| spelling | doaj-art-b86189cc494d4db6889bdbf5d4b526652025-08-20T03:50:53ZengFrontiers Media S.A.Frontiers in Earth Science2296-64632025-07-011310.3389/feart.2025.16011341601134Seismic first break picking based on multi-task learningZhongpo ZhangJing YangIntroductionSeismic first break (FB) picking helps us with near surface tomography, microseismic detection among other tasks. Using image semantic segmentation (ISS) networks to do so has been a hot topic in recent years, and multi-task learning has also demonstrated excellent data representation capabilities in several areas.MethodsTo improve accuracy, we combine the FB picking task with the seismic data reconstruction task, and propose an enhanced FB picking training method based on multi-task learning network. Specifically, we use two decoding branches of the same size in the network, which are the ISS decoding branch for the FB picking task, and the seismic feature learning decoding branch for the reconstruction task. The introduction of the seismic feature learning decoding branch will further help the network encoder to extract seismic effective features more efficiently, which will improve the accuracy of the ISS decoding branch, and ultimately improve the accuracy of the FB picking. During the training process, we use different loss functions for different decoding branches, and guide the network fitting through joint loss. In addition, we randomly add noise as well as random elimination to the seismic data to simulate the low SNR trace sets and bad traces that may exist in seismic data acquisition, and discuss the impact of different cases on the training results.Results and discussionThe experimental results show that this method achieves more accurate FB picking results than the existing single-branch ISS methods, with an average picking error as low as 3.08 ms in the field data, and the percentage of traces with a picking error higher than 15 samples is as low as 0.03%, which is far superior to the network methods such as UNet, STUNet, SegNet, and Res-Unet, and effectively realizes the overall high-quality FB picking.https://www.frontiersin.org/articles/10.3389/feart.2025.1601134/fullseismic first break (FB)image semantic segmentationFB pickingmulti-task learningseismic data reconstruction |
| spellingShingle | Zhongpo Zhang Jing Yang Seismic first break picking based on multi-task learning Frontiers in Earth Science seismic first break (FB) image semantic segmentation FB picking multi-task learning seismic data reconstruction |
| title | Seismic first break picking based on multi-task learning |
| title_full | Seismic first break picking based on multi-task learning |
| title_fullStr | Seismic first break picking based on multi-task learning |
| title_full_unstemmed | Seismic first break picking based on multi-task learning |
| title_short | Seismic first break picking based on multi-task learning |
| title_sort | seismic first break picking based on multi task learning |
| topic | seismic first break (FB) image semantic segmentation FB picking multi-task learning seismic data reconstruction |
| url | https://www.frontiersin.org/articles/10.3389/feart.2025.1601134/full |
| work_keys_str_mv | AT zhongpozhang seismicfirstbreakpickingbasedonmultitasklearning AT jingyang seismicfirstbreakpickingbasedonmultitasklearning |