Feng Qian

Orcid: 0000-0002-4761-3598

Affiliations:
  • University of Electronic Science and Technology of China, UESTC, School of Information and Communication Engineering, Chengdu, China
  • Columbia University, New York, NY, USA (2014-2015)
  • University of Electronic Science and Technology of China, Center for Information Geoscience, China (PhD 2008)


According to our database1, Feng Qian authored at least 29 papers between 2006 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Bibliography

2024
Unsupervised Intense VSP Coupling Noise Suppression With Iterative Robust Deep Learning.
IEEE Trans. Geosci. Remote. Sens., 2024

Unsupervised 3-D Seismic Erratic Noise Attenuation With Robust Tensor Deep Learning.
IEEE Trans. Geosci. Remote. Sens., 2024

Limited-Label Multiscale Deep-Learning Multihorizon Tracking.
IEEE Trans. Geosci. Remote. Sens., 2024

Trace-by-Trace Iterative VSP Wavefield Separation.
IEEE Geosci. Remote. Sens. Lett., 2024

2023
Unsupervised Seismic Footprint Removal With Physical Prior Augmented Deep Autoencoder.
IEEE Trans. Geosci. Remote. Sens., 2023

Improved Low-Rank Tensor Approximation for Seismic Random Plus Footprint Noise Suppression.
IEEE Trans. Geosci. Remote. Sens., 2023

Unsupervised Seismic Facies Deep Clustering Via Lognormal Mixture-Based Variational Autoencoder.
IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens., 2023

Unsupervised Seismic Facies Analysis via Class-Imbalanced Deep Embedding Clustering.
IEEE Geosci. Remote. Sens. Lett., 2023

Seismic Volumetric Local Slope Estimation Using Multiscale Gradient Structure Tensor.
IEEE Geosci. Remote. Sens. Lett., 2023

Multiple Attribute Regression Network for 3-D Seismic Horizon Tracking.
IEEE Geosci. Remote. Sens. Lett., 2023

2022
Multidimensional Seismic Data Denoising Using Framelet-Based Order-p Tensor Deep Learning.
IEEE Trans. Geosci. Remote. Sens., 2022

Ground Truth-Free 3-D Seismic Random Noise Attenuation via Deep Tensor Convolutional Neural Networks in the Time-Frequency Domain.
IEEE Trans. Geosci. Remote. Sens., 2022

DTAE: Deep Tensor Autoencoder for 3-D Seismic Data Interpolation.
IEEE Trans. Geosci. Remote. Sens., 2022

Unsupervised Erratic Seismic Noise Attenuation With Robust Deep Convolutional Autoencoders.
IEEE Trans. Geosci. Remote. Sens., 2022

Multiscale Adaptive Side Window Filtering and Its Application on Seismic Data.
IEEE Geosci. Remote. Sens. Lett., 2022

2021
Tubal-Sampling: Bridging Tensor and Matrix Completion in 3-D Seismic Data Reconstruction.
IEEE Trans. Geosci. Remote. Sens., 2021

3-D Seismic Noise Attenuation via Tensor Sparse Coding With Spatially Adaptive Coherence Constraint.
IEEE Access, 2021

Transitive Transfer Sparse Coding for Distant Domain.
Proceedings of the IEEE International Conference on Acoustics, 2021

2019
Identify Congested Links with Network Tomography Under Multipath Routing.
J. Netw. Syst. Manag., 2019

Tensor Super-resolution for Seismic Data.
Proceedings of the IEEE International Conference on Acoustics, 2019

2018
Tensor Sensing for Rf Tomographic Imaging.
Proceedings of the 2018 IEEE International Conference on Multimedia and Expo, 2018

Tensor-Generative Adversarial Network with Two-Dimensional Sparse Coding: Application to Real-Time Indoor Localization.
Proceedings of the 2018 IEEE International Conference on Communications, 2018

2017
Multidimensional Data Tensor Sensing for RF Tomographic Imaging.
CoRR, 2017

3D seismic data denoising using two-dimensional sparse coding scheme.
CoRR, 2017

Seismic facies recognition based on prestack data using deep convolutional autoencoder.
CoRR, 2017

Exact 3D seismic data reconstruction using Tubal-Alt-Min algorithm.
CoRR, 2017

2016
Identify Congested Links Based on Enlarged State Space.
J. Comput. Sci. Technol., 2016

Network Topology Tomography Under Multipath Routing.
IEEE Commun. Lett., 2016

2006
Recurrent Neural Network Inference of Internal Delays in Nonstationary Data Network.
Proceedings of the Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28, 2006


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