Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series

Jesper Sören Dramsch; Mikael Lüthje

doi:10.3997/2214-4609.201803020

Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series

Jesper Sören Dramsch, Mikael Lüthje

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

137 Downloads (Pure)

Abstract

Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.

Original language	English
Title of host publication	Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018)
Publisher	European Association of Geoscientists and Engineers
Publication date	2018
Pages	46-48
ISBN (Print)	978-1-5108-7668-2
DOIs	https://doi.org/10.3997/2214-4609.201803020
Publication status	Published - 2018
Event	First EAGE/PESGB Workshop Machine Learning - Olympia London, London, United Kingdom Duration: 29 Nov 2018 → 30 Nov 2018

Conference

Conference	First EAGE/PESGB Workshop Machine Learning
Location	Olympia London
Country	United Kingdom
City	London
Period	29/11/2018 → 30/11/2018

Access to Document

10.3997/2214-4609.201803020

Information Theory Considerations in Patch based Training of Deep Neural Networks on Seismic Time SeriesFinal published version, 332 KB

Fingerprint Dive into the research topics of 'Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series'. Together they form a unique fingerprint.

Cite this

@inproceedings{8d0b55533543454ab495bb42ae3429a9,

title = "Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series",

abstract = "Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability. ",

author = "{S{\"o}ren Dramsch}, Jesper and Mikael L{\"u}thje",

year = "2018",

doi = "10.3997/2214-4609.201803020",

language = "English",

isbn = "978-1-5108-7668-2",

pages = "46--48",

booktitle = "Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018)",

publisher = "European Association of Geoscientists and Engineers",

note = "First EAGE/PESGB Workshop Machine Learning ; Conference date: 29-11-2018 Through 30-11-2018",

}

Sören Dramsch, J & Lüthje, M 2018, Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series. in Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018). European Association of Geoscientists and Engineers, pp. 46-48, First EAGE/PESGB Workshop Machine Learning , London, United Kingdom, 29/11/2018. https://doi.org/10.3997/2214-4609.201803020

Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series. / Sören Dramsch, Jesper; Lüthje, Mikael.

Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018). European Association of Geoscientists and Engineers, 2018. p. 46-48.

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Information Theory Considerations in Patch-based Training of Deep Neural Networks on Seismic Time-Series

AU - Sören Dramsch, Jesper

AU - Lüthje, Mikael

PY - 2018

Y1 - 2018

N2 - Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.

AB - Recent advances in machine learning relies on convolutional deep neural networks. These are often trained on cropped image patches. Pertaining to non-stationary seismic signals this may introduce low frequency noise and non-generalizability.

U2 - 10.3997/2214-4609.201803020

DO - 10.3997/2214-4609.201803020

M3 - Article in proceedings

SN - 978-1-5108-7668-2

SP - 46

EP - 48

BT - Proceedings of the First EAGE/PESGB Workshop on Machine Learning (London2018)

PB - European Association of Geoscientists and Engineers

T2 - First EAGE/PESGB Workshop Machine Learning

Y2 - 29 November 2018 through 30 November 2018

ER -