eprintid: 20643
rev_number: 3
eprint_status: archive
userid: 1
dir: disk0/00/02/06/43
datestamp: 2026-07-21 06:29:12
lastmod: 2026-07-21 06:29:12
status_changed: 2026-07-21 06:29:12
type: article
metadata_visibility: show
creators_name: Babikir, Ismailalwali
creators_name: Latiff, Abdul Halim Abdul
creators_name: Hassan, N.N. Anis Amalina N.M.
creators_name: Alakbari, Fahd Saeed
title: Comparative analysis of traditional machine learning and deep learning for seismic facies classification using F3 data from the Dutch North Sea
ispublished: pub
note: Cited by: 0
date: 2026
publisher: Elsevier B.V.
official_url: https://www.scopus.com/pages/publications/105033860303?origin=resultslist
id_number: 10.1016/j.jappgeo.2026.106224
full_text_status: none
publication: Journal of Applied Geophysics
volume: 250
refereed: TRUE
issn: 09269851
citation:   Babikir, Ismailalwali and Latiff, Abdul Halim Abdul and Hassan, N.N. Anis Amalina N.M. and Alakbari, Fahd Saeed  (2026) Comparative analysis of traditional machine learning and deep learning for seismic facies classification using F3 data from the Dutch North Sea.  Journal of Applied Geophysics, 250.   ISSN 09269851