Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging-Reference-Cited by-同舟云学术

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Unsupervised deep domain adaptation framework in remote acoustic time parametric imaging

Published:2021-07-29 Issue: Volume: Page:
ISSN:1751-9675
Container-title:IET Signal Processing
language:en
Short-container-title:IET Signal Processing

Author:

Wu Yuezhou¹^ORCID,Tao Jun²,Fu Qiang¹,Liu Changjiang³

Affiliation:

1. School of Computer Science Civil Aviation Flight University of China Guanghan Sichuan China

2. College of Physics Sichuan University Chengdu Sichuan China

3. College of Mathematics and Statistics Sichuan University of Science and Engineering Zigong Sichuan China

Funder

National Natural Science Foundation of China

Publisher

Institution of Engineering and Technology (IET)

Subject

Electrical and Electronic Engineering,Signal Processing

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1049/sil2.12071

Reference59 articles.

1. Imaging the Chicxulub Central Crater Zone from Large-Scale Seismic Acoustic Wave Propagation and Gravity Modeling

2. Fracture identification based on remote detection acoustic reflection logging

3. Electromagnetic logging technique based on borehole radar

4. Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

5. Extracting Reflected Waves From Acoustic Logging Data Based on the Shearlet Transform

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