Zobrazeno 1 - 10 of 102 pro vyhledávání: '"Sanyi Yuan"'
2
Porosity prediction using semi-supervised learning with biased well log data for improving estimation accuracy and reducing prediction uncertainty
Autor: Wenjing Sang, Sanyi Yuan, Hongwei Han, Haojie Liu, Yue Yu
Publikováno v: Geophysical Journal International. 232:940-957
SUMMARY Porosity characterization is of profound significance for seismic inversion and hydrocarbon prediction. Although semi-supervised learning (SSL) based methods have been used to boost prediction accuracy and lateral continuity of supervised lea
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::b17de3c73c68f5a9b5e87361a51dae06
https://doi.org/10.1093/gji/ggac371
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3
Automatic velocity analysis using interpretable multi-mode neural networks
Autor: Haifeng Zhang, Sanyi Yuan, Huan Yuan, Huahui Zeng, Yang Gao, Shangxu Wang
Publikováno v: Geophysical Journal International.
Summary Seismic velocity analysis is the basis for seismic imaging and understanding complex subsurface geological structures. Although the performance of automatic velocity analysis methods based on CMP data or velocity spectra is encouraging, parti
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::5a3c00bd4f3ef338e7a0eb644c100db1
https://doi.org/10.1093/gji/ggad202
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4
SegNet-based first-break picking via seismic waveform classification directly from shot gathers with sparsely distributed traces
Autor: Tao Xie, Shangxu Wang, Jie Qi, Sanyi Yuan, Yue Zhao
Publikováno v: Petroleum Science. 19:162-179
Manually picking regularly and densely distributed first breaks (FBs) are critical for shallow velocity-model building in seismic data processing. However, it is time consuming. We employ the fully-convolutional SegNet to address this issue and prese
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ac554f36a02142bfb69af0dfa1afbb7f
https://doi.org/10.1016/j.petsci.2021.10.010
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10
Double-scale supervised inversion with a data-driven forward model for low-frequency impedance recovery
Autor: Sanyi Yuan, Wenjing Sang, Yaneng Luo, Jiao Xinqi, Shangxu Wang
Publikováno v: GEOPHYSICS. 87:R165-R181
Low-frequency information is important in reducing the nonuniqueness of absolute impedance inversion and for quantitative seismic interpretation. In traditional model-driven impedance inversion methods, the low-frequency impedance background is from
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::8541ddbdfbaeaf24e2706ae374076641
https://doi.org/10.1190/geo2020-0421.1
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