First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data
Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, w...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-01-01
|
| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/15/2/356 |
| _version_ | 1827622246976323584 |
|---|---|
| author | Yuchen Yin Liguo Han Pan Zhang Zhanwu Lu Xujia Shang |
| author_facet | Yuchen Yin Liguo Han Pan Zhang Zhanwu Lu Xujia Shang |
| author_sort | Yuchen Yin |
| collection | DOAJ |
| description | Deep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high. |
| first_indexed | 2024-03-09T11:19:53Z |
| format | Article |
| id | doaj.art-0d4a5bb675e745e3b77b03ab957ec09b |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-03-09T11:19:53Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-0d4a5bb675e745e3b77b03ab957ec09b2023-12-01T00:19:26ZengMDPI AGRemote Sensing2072-42922023-01-0115235610.3390/rs15020356First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted DataYuchen Yin0Liguo Han1Pan Zhang2Zhanwu Lu3Xujia Shang4College of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaInstitute of Geology, Chinese Academy of Geological Sciences, Beijing 100037, ChinaCollege of Geo-Exploration Science and Technology, Jilin University, Changchun 130026, ChinaDeep reflection seismic data are usually accompanied by large-offset data, and the accurate and rapid identification of the first arrivals of seismic records plays an important role in eliminating the effects of topography and other factors that increase with the increasing offsets. In this paper, we propose a method based on convolutional neural networks (CNNs) that can accurately identify the first arrivals of large-offset seismic data. A time window for linear dynamic correction was established to convert the raw seismic data into rectangular images so as to reduce the amount of invalid sample data and improve the training efficiency. In order to enhance the prediction effect of the far-offset first arrivals, we propose the strategy of adjusting the weight of the far-offset data to increase the weight of the far-offset data in the training dataset and, thus, to improve the first arrival accuracy. The manually picked first arrivals are used as labels and the input to the CNNs for training, and the full-offset first arrivals are the output. The travel time tomography velocity is modeled and compared based on the first arrivals obtained through manual picking, industrial software automatic picking, and CNN prediction. The results show that the application of CNNs to large-offset seismic datasets can help researchers to obtain the first arrivals at different offsets, while the inclusion of far-offset weights can effectively improve the modeling depth of the tomography inversion, and the accuracy of the results is high.https://www.mdpi.com/2072-4292/15/2/356first-break pickinglarge-offset seismic datadeep learningconvolutional neural networktomography images |
| spellingShingle | Yuchen Yin Liguo Han Pan Zhang Zhanwu Lu Xujia Shang First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data Remote Sensing first-break picking large-offset seismic data deep learning convolutional neural network tomography images |
| title | First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data |
| title_full | First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data |
| title_fullStr | First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data |
| title_full_unstemmed | First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data |
| title_short | First-Break Picking of Large-Offset Seismic Data Based on CNNs with Weighted Data |
| title_sort | first break picking of large offset seismic data based on cnns with weighted data |
| topic | first-break picking large-offset seismic data deep learning convolutional neural network tomography images |
| url | https://www.mdpi.com/2072-4292/15/2/356 |
| work_keys_str_mv | AT yuchenyin firstbreakpickingoflargeoffsetseismicdatabasedoncnnswithweighteddata AT liguohan firstbreakpickingoflargeoffsetseismicdatabasedoncnnswithweighteddata AT panzhang firstbreakpickingoflargeoffsetseismicdatabasedoncnnswithweighteddata AT zhanwulu firstbreakpickingoflargeoffsetseismicdatabasedoncnnswithweighteddata AT xujiashang firstbreakpickingoflargeoffsetseismicdatabasedoncnnswithweighteddata |