Zobrazeno 1 - 10
of 100
pro vyhledávání: '"Naihao Liu"'
Publikováno v:
Artificial Intelligence in Geosciences, Vol 4, Iss , Pp 128-136 (2023)
The stratigraphic correlation of well logs plays an essential role in characterizing subsurface reservoirs. However, it suffers from a small amount of training data and expensive computing time. In this work, we propose the Attention Based Dense Netw
Externí odkaz:
https://doaj.org/article/37918f6c9559408f991a0cb5337c4b59
Publikováno v:
IEEE Access, Vol 11, Pp 99693-99704 (2023)
One of the major hot topics in seismic data processing is the reconstruction of successively sampled seismic data. There are numerous traditional methods proposed for addressing this issue; however, they still have unavoidable drawbacks, such as high
Externí odkaz:
https://doaj.org/article/96b598f941d54cfc83b16a71e51b7e88
Publikováno v:
Artificial Intelligence in Geosciences, Vol 3, Iss , Pp 192-202 (2022)
Seismic data interpolation, especially irregularly sampled data interpolation, is a critical task for seismic processing and subsequent interpretation. Recently, with the development of machine learning and deep learning, convolutional neural network
Externí odkaz:
https://doaj.org/article/eeb068b8f4344a899ab07b396e5973af
Publikováno v:
Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 223-233 (2021)
Seismic random noise reduction is an important task in seismic data processing at the Chinese loess plateau area, which benefits the geologic structure interpretation and further reservoir prediction. The sparse inversion is one of the widely used to
Externí odkaz:
https://doaj.org/article/93358b1204a74773907bfc3b16ba29e9
Publikováno v:
GEOPHYSICS. :1-59
Time-frequency (TF) transforms are commonly used to analyze local features of non-stationary seismic data and to help uncover structural or geological information. Traditional TF transforms, such as short-time Fourier transform (STFT), continuous wav
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::67b923f711ddb236da0e1cdfc5fd7a2a
https://doi.org/10.1190/geo2022-0212.1
https://doi.org/10.1190/geo2022-0212.1
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 20:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6f228effbef7a826cdd13230f1739b12
https://doi.org/10.1109/lgrs.2023.3243005
https://doi.org/10.1109/lgrs.2023.3243005
Publikováno v:
IEEE Signal Processing Letters. 30:55-59
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8a1161e73ce5fe9139b03f49fcd81218
https://doi.org/10.1109/lsp.2023.3235646
https://doi.org/10.1109/lsp.2023.3235646
Publikováno v:
Interpretation. 10:T619-T636
Recently, the computation of seismic fault attribute that may be significant in seismic interpretation is that seismic fault detection is treated as an image segmentation problem using different deep-learning (DL) architectures. To do this, researche
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::012bb49760116057521af44bc38392a8
https://doi.org/10.1190/int-2022-0007.1
https://doi.org/10.1190/int-2022-0007.1
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-13
Lithology interpretation is important for understanding subsurface properties. Yet, the common manual well log interpretation is usually with low efficiency and bad consistency. Therefore, the automatic well log interpretation tools based on machine
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a884c1615b3e14e91c96f4e68e4fb216
https://doi.org/10.1109/tgrs.2021.3085340
https://doi.org/10.1109/tgrs.2021.3085340
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0deb002276320eda8df1c0e36f8891f2
https://doi.org/10.1109/lgrs.2021.3107477
https://doi.org/10.1109/lgrs.2021.3107477