Zobrazeno 1 - 10
of 80
pro vyhledávání: '"Shaohuan Zu"'
Publikováno v:
Frontiers in Earth Science, Vol 11 (2023)
Externí odkaz:
https://doaj.org/article/1e146b4735d043dcaeeb9d9a720b064e
Publikováno v:
Earth and Space Science, Vol 6, Iss 7, Pp 1244-1261 (2019)
Automatic waveform classification and arrival picking methods are widely studied to reduce or replace the manual works. Machine learning based methods, especially neural networks, and clustering based methods have shown great potentials in previous s
Externí odkaz:
https://doaj.org/article/4c6f7cd102a446d0a19664cccf3073ff
Publikováno v:
Geophysical Prospecting. 70:847-864
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6ca1984fa4b481591f957e49c52935b8
https://doi.org/10.1111/1365-2478.13208
https://doi.org/10.1111/1365-2478.13208
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 19:1-5
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5795fa22079fd94948206cc4c8ca085a
https://doi.org/10.1109/lgrs.2022.3174106
https://doi.org/10.1109/lgrs.2022.3174106
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::2ffd3cfc7c9404933704d95e2d3c7af5
https://doi.org/10.1109/tgrs.2022.3179518
https://doi.org/10.1109/tgrs.2022.3179518
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-10
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f6469a6ff1ed150b2c061f58608d45fe
https://doi.org/10.1109/tgrs.2022.3192531
https://doi.org/10.1109/tgrs.2022.3192531
Publikováno v:
IEEE Geoscience and Remote Sensing Letters. 18:1675-1679
Acquisition technology of multisource data has outstanding advantages in enhancing collection efficiency and reducing cost. However, traditional seismic data processes cannot be applied to multisource blended data. Therefore, deblending technology of
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::672cff8f429f13a36d08c6870b3f0e32
https://doi.org/10.1109/lgrs.2020.3003803
https://doi.org/10.1109/lgrs.2020.3003803
Publikováno v:
Geoscience Frontiers. 14:101567
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::91c65d0f4806098000eba54963a2ff5a
https://doi.org/10.1016/j.gsf.2023.101567
https://doi.org/10.1016/j.gsf.2023.101567
Publikováno v:
GEOPHYSICS. 86:V419-V429
The prediction-error filter (PEF) assumes that seismic data can be destructed to zero by applying a convolutional operation between the target data and the prediction filter in either the time-space or frequency-space domain. We have extended the com
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a044bedb4909a0fb40109fbd72d5856a
https://doi.org/10.1190/geo2020-0540.1
https://doi.org/10.1190/geo2020-0540.1
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 59:7098-7108
A sparse dictionary is more adaptive than a sparse fixed-basis transform since it can learn the features directly from the input data in a data-driven way. However, learning a sparse dictionary is time-consuming because a large number of iterations a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4375a83ba4e595820861875dae703813
https://doi.org/10.1109/tgrs.2020.3030740
https://doi.org/10.1109/tgrs.2020.3030740