Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes-Reference-Cited by-同舟云学术

Self-Supervised Learning for Seismic Data: Enhancing Model Interpretability With Seismic Attributes

Published:2023 Issue: Volume:61 Page:1-18
ISSN:0196-2892
Container-title:IEEE Transactions on Geoscience and Remote Sensing
language:
Short-container-title:IEEE Trans. Geosci. Remote Sensing

Author:

Salazar Jose Julian¹^ORCID,Maldonado-Cruz Eduardo¹,Ochoa Jesus²,Pyrcz Michael J.³^ORCID

Affiliation:

1. Hildebrand Department of Petroleum and Geosystems Engineering, Cockrell School of Engineering, The University of Texas at Austin, Austin, TX, USA

2. Technology, Digital & Innovation, Equinor US, Houston, TX, USA

3. Hildebrand Department of Petroleum and Geosystems Engineering, Department of Geological Sciences, Cockrell School of Engineering, Jackson School of Geosciences, The University of Texas at Austin, Austin, TX, USA

Funder

DIRECT Consortium

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Earth and Planetary Sciences,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/36/10006360/10149445.pdf?arnumber=10149445

Reference65 articles.

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3. Self-supervised visual feature learning with deep neural networks: A survey;jing;arXiv 1902 06162,2019

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