Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Oluwaseun Joseph Aribido"'
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-12
In recent years, deep neural networks have significantly impacted the seismic interpretation process. Due to the simple implementation and low interpretation costs, deep neural networks are an attractive component for the common interpretation pipeli
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa462c997350cf7bcc12cda6bb4d8746
https://doi.org/10.1109/tgrs.2022.3178112
https://doi.org/10.1109/tgrs.2022.3178112
Even though deep neural networks have shown tremendous success in countless applications, explaining model behaviour or predictions is an open research problem. In this paper, we address this issue by employing a simple yet effective method by analys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::aa9dad848bd2a7d6c7213614c639ca0b
http://arxiv.org/abs/2301.04221
http://arxiv.org/abs/2301.04221
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:
First International Meeting for Applied Geoscience & Energy Expanded Abstracts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79e595749a893414fca8a6f16af0dce0
https://doi.org/10.1190/segam2021-3583956.1
https://doi.org/10.1190/segam2021-3583956.1
Publikováno v:
First International Meeting for Applied Geoscience & Energy Expanded Abstracts.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d2635499fe5a536dbd10b1830889e733
https://doi.org/10.1190/segam2021-3583675.1
https://doi.org/10.1190/segam2021-3583675.1
Publikováno v:
ICIP
Annotating seismic data is expensive, laborious and subjective due to the number of years required for seismic interpreters to attain proficiency in interpretation. In this paper, we develop a framework to automate annotating pixels of a seismic imag
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e5b669c6d886a138fdfecd27780fc6f
http://arxiv.org/abs/2009.04631
http://arxiv.org/abs/2009.04631