Zobrazeno 1 - 10
of 24
pro vyhledávání: '"Jun-Xing Cao"'
Publikováno v:
Artificial Intelligence in Geosciences, Vol 2, Iss , Pp 107-114 (2021)
A direct hydrocarbon detection is performed by using multi-attributes based quantum neural networks with gas fields. The proposed multi-attributes based quantum neural networks for hydrocarbon detection use data clustering and local wave decompositio
Externí odkaz:
https://doaj.org/article/cc8350d01ca04a6da82347b6260ab983
Publikováno v:
Petroleum Science (KeAi Communications Co.); Jun2024, Vol. 21 Issue 3, p1649-1659, 11p
Autor:
Jian-Yong Xie, Yan-Ping Fang, Xing-Hua Wu, Jian'er Zhao, Jun-Cheng Dai, Jun-Xing Cao, Ji-Xin Deng
Publikováno v:
Petroleum Science (KeAi Communications Co.); Apr2024, Vol. 21 Issue 2, p855-865, 11p
Publikováno v:
Geophysical Journal International. 233:1950-1959
SUMMARYSeismic attenuation has a considerable impact on resolution reduction and the increase in the dominant frequency period of seismic data. The absorption coefficient estimates, which measure inelastic attenuation, provide a deep understanding of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bbe8fcd43c4cca507adb319fa0cc01c7
https://doi.org/10.1093/gji/ggad042
https://doi.org/10.1093/gji/ggad042
Autor:
Jian-Yong Xie, Jun-Jie Zhang, Wei Xiang, Yan-Ping Fang, Ya-Juan Xue, Jun-Xing Cao, Ren-Fei Tian
Publikováno v:
Petroleum Science. 19:2683-2694
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0111119faec07a0aee58927610a9a2fc
https://doi.org/10.1016/j.petsci.2022.06.009
https://doi.org/10.1016/j.petsci.2022.06.009
Publikováno v:
GEOPHYSICS. 87:V261-V277
The quality factor Q is generally used to describe seismic attenuation that leads to amplitude decay (AD) and wavelet distortion. Time-frequency transforms are commonly used to measure quality factor Q on surface seismic data. These methods capture f
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::68ebde71e496d6f5c6215e7485e1b19e
https://doi.org/10.1190/geo2021-0210.1
https://doi.org/10.1190/geo2021-0210.1
Autor:
Ya-Juan Xue, Xing-Jian Wang, Jun-Xing Cao, Hao-Kun Du, Jian-Yong Xie, Jia-Chun You, Xu-Dong Jiang, Jia Yang
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-11
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::411741efdfa9be4151b72009d2b0f471
https://doi.org/10.1109/tgrs.2022.3217274
https://doi.org/10.1109/tgrs.2022.3217274
Publikováno v:
AIP Conference Proceedings.
In the study of shale oil and gas reservoirs prediction, the total organic carbon content (TOC) is one of the important indexes to evaluate its hydrocarbon generation capability. Therefore, an accurate method for predicting TOC is particularly signif
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::60600ca2ff07a39ab557afe5fb8cbe2e
https://doi.org/10.1063/1.5012435
https://doi.org/10.1063/1.5012435
Publikováno v:
Chinese Journal of Geophysics. 54:864-869
This paper presents a dual-parameter regularization method with a term that has a second order regularization operator. The optimal value of the regularization parameter is determined by applying the L-curve criterion, the discrepancy principle and g
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::034390f25a84265cbd13d7a5e90b10db
https://doi.org/10.1002/cjg2.1669
https://doi.org/10.1002/cjg2.1669