دورية أكاديمية
Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA
| العنوان: | Microleveling aerogeophysical data using deep convolutional network and MoG-RPCA |
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| المؤلفون: | Xinze Li, Bangyu Wu, Guofeng Liu, Xu Zhu, Linfei Wang |
| المصدر: | Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 20-25 (2021) |
| بيانات النشر: | KeAi Communications Co. Ltd., 2021. |
| سنة النشر: | 2021 |
| المجموعة: | LCC:Geography (General) LCC:Information technology |
| مصطلحات موضوعية: | Areogeophysical data, Microleveling, Deep convolutional network, MoG-RPCA, Geography (General), G1-922, Information technology, T58.5-58.64 |
| الوصف: | Residual magnetic error remains after standard levelling process. The weak non-geological effect, manifesting itself as streaky noise along flight lines, creates a challenge for airborne geophysical data processing and interpretation. Microleveling is the process to eliminate this residual noise and is now a standard areogeophysical data processing step. In this paper, we propose a two-step procedure for single aerogeophysical data microleveling: a deep convolutional network is first adopted as approximator to map the original data into a low-level part with nature geological structures and a corrugated residual which still contains high-level detail geological structures; second, the mixture of Gaussian robust principal component analysis (MoG-RPCA) is then used to separate the weak energy fine structures from the residual. The final microleveling result is the addition of low-level structures from deep convolutional network and fine structures from MoG-RPCA. The deep convolutional network does not need dataset for training and the handcrafted network serves as prior (deep image prior) to capture the low-level nature geological structures in the areogeophysical data. Experiments on synthetic data and field data demonstrate that the combination of deep convolutional network and MoG-RPCA is an effective framework for single areogeophysical data microleveling. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2666-5441 |
| العلاقة: | http://www.sciencedirect.com/science/article/pii/S2666544121000253Test; https://doaj.org/toc/2666-5441Test |
| DOI: | 10.1016/j.aiig.2021.08.003 |
| الوصول الحر: | https://doaj.org/article/cfdf4e076ee1406fbbb5dc835a3e9fb8Test |
| رقم الانضمام: | edsdoj.fdf4e076ee1406fbbb5dc835a3e9fb8 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 26665441 |
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| DOI: | 10.1016/j.aiig.2021.08.003 |