Machine Learning and Seismic Structural Attributes Hybrid Approach to Map Complex Fault System

M. K. Khan, Y. Bashir, S. Dossary, S. S. Ali, G. Janampa Ananos

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde BölümKonferans katkısıbilirkişi

2 Atıf (Scopus)

Özet

This paper present a hybrid Machine Learning and seismic structural attributes approach to extract detailed major and minor discontinuities from seismic data from a complex fault system that present a challenge using conventional interpretation techniques. Fault interpretation is image segmentation problem and we thus adopted U-net encoder-decoder architecture as a first step in this hybrid workflow, it is well suited for seismic discontinuities. Image segmentation can not only figure out whether a particular feature such as faults exist but can also create a mask showing where in the volume those features exist.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2nd EAGE Subsurface Intelligence Workshop
YayınlayanEuropean Association of Geoscientists and Engineers, EAGE
ISBN (Elektronik)9789462824409
DOI'lar
Yayın durumuYayınlandı - 2022
Harici olarak yayınlandıEvet
Etkinlik2nd EAGE Subsurface Intelligence Workshop 2022 - Manama, Bahrain
Süre: 28 Eki 202231 Eki 2022

Yayın serisi

Adı2nd EAGE Subsurface Intelligence Workshop

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???event.eventtypes.event.conference???2nd EAGE Subsurface Intelligence Workshop 2022
Ülke/BölgeBahrain
ŞehirManama
Periyot28/10/2231/10/22

Bibliyografik not

Publisher Copyright:
© 2nd EAGE Subsurface Intelligence Workshop 2022.

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