CITATION.cff

cff-version: '1.2.0'
message: 'Please cite the following works when using this software.'
abstract: 'Deep-learning seismic facies on state-of-the-art CNN architectures'
authors:
  - family-names: 'Dramsch'
    given-names: 'Jesper Sören'
    orcid: '0000-0001-8273-905X'
identifiers:
  - type: 'url'
    value: 'https://github.com/JesperDramsch/seismic-transfer-learning'
title: 'JesperDramsch/seismic-transfer-learning'
url: 'https://github.com/JesperDramsch/seismic-transfer-learning'
abbreviation: 'seismic-transfer-learning'
date-published: 2020-08-21
year: 2020
month: 8
type: 'book'
preferred-citation:
  abstract: ' We explore propagation of seismic interpretation by deep learning in stacked 2D sections. We show the application of state-of-the-art image classification algorithms on seismic data. These algorithms were trained on big labeled photograph databases. We use transfer learning to benefit from pre-trained networks and evaluate their performance on seismic data.Presentation Date: Wednesday, October 17, 2018Start Time: 8:30:00 AMLocation: 204B (Anaheim Convention Center)Presentation Type: Oral '
  authors:
    - family-names: 'Dramsch'
      given-names: 'Jesper Sören'
      orcid: '0000-0001-8273-905X'
    - family-names: 'Lüthje'
      given-names: 'Mikael'
      orcid: "0000-0003-2715-1653"
  doi: '10.1190/segam2018-2996783.1'
  identifiers:
    - type: 'doi'
      value: '10.1190/segam2018-2996783.1'
    - type: 'url'
      value: 'https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1'
  title: 'Deep-learning seismic facies on state-of-the-art CNN architectures'
  url: 'https://library.seg.org/doi/abs/10.1190/segam2018-2996783.1'