eprintid: 16594
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/65/94
datestamp: 2023-12-19 03:23:06
lastmod: 2023-12-19 03:23:06
status_changed: 2023-12-19 03:06:32
type: article
metadata_visibility: show
creators_name: Pratama, H.
creators_name: Latiff, A.H.A.
title: Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning
ispublished: pub
note: cited By 3
abstract: A geological interpretation plays an important role to gain information about the structural and stratigraphic of hydrocarbon reservoirs. However, this is a time-consuming task due to the com-plexity and size of seismic data. We propose a semi-supervised learning technique to automatically and accurately delineate the geological features from 3D seismic data. To generate labeling data for training the supervised Convolutional Neural Network (CNN) model, we propose an efficient workflow based on unsupervised learning. This workflow utilized seismic attributes and KernelPCA to enhance the visualization of geological targets and clustering the features into binary classes using K-means approach. With this workflow, we are able to develop a data-driven model and reduce human subjectivity. We applied this technique in two cases with different geological settings. The synthetic data and the real seismic investigation from the A Field in the Malay Basin. From this application, we demonstrate that our CNN-based model is highly accurate and consistent with the previous manual interpretation in both cases. In addition to qualitatively evaluating the interpreta-tions, we further extract the predicted result into a 3D geobody. This result could help the interpreter focus on tasks requiring human expertise and aid the model�s prediction in the next studies. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.
date: 2022
publisher: MDPI
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85133677820&doi=10.3390%2fapp12136723&partnerID=40&md5=900b9ff5da770a934397bf14cdf6bfc5
id_number: 10.3390/app12136723
full_text_status: none
publication: Applied Sciences (Switzerland)
volume: 12
number: 13
refereed: TRUE
issn: 20763417
citation:   Pratama, H. and Latiff, A.H.A.  (2022) Automated Geological Features Detection in 3D Seismic Data Using Semi-Supervised Learning.  Applied Sciences (Switzerland), 12 (13).   ISSN 20763417