A gigabyte interpreted seismic dataset for automatic fault recognition
The lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today’s AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic images and their corresponding fault annotations. Th...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2021-08-01
|
| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340921005035 |
| _version_ | 1818657106486624256 |
|---|---|
| author | Yu An Jiulin Guo Qing Ye Conrad Childs John Walsh Ruihai Dong |
| author_facet | Yu An Jiulin Guo Qing Ye Conrad Childs John Walsh Ruihai Dong |
| author_sort | Yu An |
| collection | DOAJ |
| description | The lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today’s AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic images and their corresponding fault annotations. The processed seismic images, which are originally from a seismic survey called Thebe Gas Field in the Exmouth Plateau of the Carnarvan Basin on the NW shelf of Australia, are represented in Python Numpy format, which can be easily adopted by various AI models and will facilitate cooperation with researchers in the field of computer science. The corresponding fault annotations were firstly manually labelled by expert interpreters of faults from seismic data in order to investigate the structural style and associated evolution of the basin. Then the fault interpretation and seismic survey are processed and collected using Petrel software and Python programs separately. This dataset can help to train, validate, and evaluate the performance of different automatic fault recognition workflow. |
| first_indexed | 2024-12-17T03:36:12Z |
| format | Article |
| id | doaj.art-d5b50824682149af981092f05818db92 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-12-17T03:36:12Z |
| publishDate | 2021-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-d5b50824682149af981092f05818db922022-12-21T22:05:08ZengElsevierData in Brief2352-34092021-08-0137107219A gigabyte interpreted seismic dataset for automatic fault recognitionYu An0Jiulin Guo1Qing Ye2Conrad Childs3John Walsh4Ruihai Dong5Corresponding author.; The Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, IrelandC&C Reservoirs, Brunel House, Reading, United KingdomKey Laboratory of Tectonics and Petroleum Resources, Ministry of Education, China University of Geosciences, Wuhan 430074, ChinaFault Analysis Group, School of Earth Sciences, University College Dublin, Dublin, IrelandFault Analysis Group, School of Earth Sciences, University College Dublin, Dublin, Ireland; iCRAG (Irish Centre for Research in Applied Geosciences), IrelandThe Insight Centre for Data Analytics, School of Computer Science, University College Dublin, Dublin, IrelandThe lack of large-scale open-source expert-labelled seismic datasets is one of the barriers to applying today’s AI techniques to automatic fault recognition tasks. The dataset present in this article consists of a large number of processed seismic images and their corresponding fault annotations. The processed seismic images, which are originally from a seismic survey called Thebe Gas Field in the Exmouth Plateau of the Carnarvan Basin on the NW shelf of Australia, are represented in Python Numpy format, which can be easily adopted by various AI models and will facilitate cooperation with researchers in the field of computer science. The corresponding fault annotations were firstly manually labelled by expert interpreters of faults from seismic data in order to investigate the structural style and associated evolution of the basin. Then the fault interpretation and seismic survey are processed and collected using Petrel software and Python programs separately. This dataset can help to train, validate, and evaluate the performance of different automatic fault recognition workflow.http://www.sciencedirect.com/science/article/pii/S2352340921005035Fault recognitionSeismic interpretationComputer visionImage processing |
| spellingShingle | Yu An Jiulin Guo Qing Ye Conrad Childs John Walsh Ruihai Dong A gigabyte interpreted seismic dataset for automatic fault recognition Data in Brief Fault recognition Seismic interpretation Computer vision Image processing |
| title | A gigabyte interpreted seismic dataset for automatic fault recognition |
| title_full | A gigabyte interpreted seismic dataset for automatic fault recognition |
| title_fullStr | A gigabyte interpreted seismic dataset for automatic fault recognition |
| title_full_unstemmed | A gigabyte interpreted seismic dataset for automatic fault recognition |
| title_short | A gigabyte interpreted seismic dataset for automatic fault recognition |
| title_sort | gigabyte interpreted seismic dataset for automatic fault recognition |
| topic | Fault recognition Seismic interpretation Computer vision Image processing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340921005035 |
| work_keys_str_mv | AT yuan agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT jiulinguo agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT qingye agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT conradchilds agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT johnwalsh agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT ruihaidong agigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT yuan gigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT jiulinguo gigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT qingye gigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT conradchilds gigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT johnwalsh gigabyteinterpretedseismicdatasetforautomaticfaultrecognition AT ruihaidong gigabyteinterpretedseismicdatasetforautomaticfaultrecognition |