Zobrazeno 1 - 10
of 23
pro vyhledávání: '"Yazeed Alaudah"'
Autor:
Mirco Milletarì, Sara Malvar, Yagna D. Oruganti, Leonardo O. Nunes, Yazeed Alaudah, Anirudh Badam
Publikováno v:
Machine Learning, Optimization, and Data Science ISBN: 9783031255984
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ffbccbcfc400c764222393f80ccee2f9
https://doi.org/10.1007/978-3-031-25599-1_21
https://doi.org/10.1007/978-3-031-25599-1_21
Publikováno v:
GEOPHYSICS. 86:V497-V508
We have developed two machine-learning frameworks that could assist in automated lithostratigraphic interpretation of seismic volumes without any manual hand labeling from an experienced seismic interpreter. The first framework is an unsupervised hie
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b738f9a2b854d91a253646ffa44f2735
https://doi.org/10.1190/geo2020-0541.1
https://doi.org/10.1190/geo2020-0541.1
Publikováno v:
Interpretation. 7:SE175-SE187
The recent interest in using deep learning for seismic interpretation tasks, such as facies classification, has been facing a significant obstacle, namely, the absence of large publicly available annotated data sets for training and testing models. A
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::21dc1a03d62f8d128ac9832d7bd0b20f
https://doi.org/10.1190/int-2018-0249.1
https://doi.org/10.1190/int-2018-0249.1
Publikováno v:
The Leading Edge. 37:451-461
As a process that identifies geologic structures of interest such as faults, salt domes, or elements of petroleum systems in general, seismic structural interpretation depends heavily on the domain knowledge and experience of interpreters as well as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fbbf24a4ec7d91ef97db07003648a5de
https://doi.org/10.1190/tle37060451.1
https://doi.org/10.1190/tle37060451.1
Publikováno v:
SEG Technical Program Expanded Abstracts 2019.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0f5b6732c400ce4bcd96d6b44519d48b
https://doi.org/10.1190/segam2019-3216766.1
https://doi.org/10.1190/segam2019-3216766.1
Recently, there has been significant interest in various supervised machine learning techniques that can help reduce the time and effort consumed by manual interpretation workflows. However, most successful supervised machine learning algorithms requ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5060cb7e634efd8faf6e72ee31bc1d5
http://arxiv.org/abs/1905.06528
http://arxiv.org/abs/1905.06528
Autor:
Yazeed Alaudah, Ghassan AlRegib
The coherence attribute is one of the most commonly used attributes in seismic interpretation. In this paper, we propose building on the recently introduced Generalized Tensor-based Coherence (GTC) attribute to make it directionally selective. This d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f87e84b570aeabd730c8ce204e89d6e5
http://arxiv.org/abs/1901.05307
http://arxiv.org/abs/1901.05307
We explore the use of multiresolution analysis techniques as texture attributes for seismic image characterization, especially in representing subsurface structures in large migrated seismic data. Namely, we explore the Gaussian pyramid, the discrete
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0ff0499011d40f53c122cca74193f0f1
Autor:
Mohamed Deriche, Motaz Alfarraj, Yazeed Alaudah, Muhammad Ali Qureshi, Asjad Amin, Ghassan AlRegib, Zhen Wang, Zhiling Long, Yuting Hu, Suhail Al-Dharrab, Haibin Di
In this paper, we explore how to computationally characterize subsurface geological structures presented in seismic volumes using texture attributes. For this purpose, we conduct a comparative study of typical texture attributes presented in the imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::57dcb210167fd8c91355f9d706e0c057
http://arxiv.org/abs/1812.08263
http://arxiv.org/abs/1812.08263
Publikováno v:
SEG Technical Program Expanded Abstracts 2018.
Recently, there has been increasing interest in using deep learning techniques for various seismic interpretation tasks. However, unlike shallow machine learning models, deep learning models are often far more complex and can have hundreds of million
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0cff2dbf47c8197efd4325658bb02f92
https://doi.org/10.1190/segam2018-2997865.1
https://doi.org/10.1190/segam2018-2997865.1