Deep-learning inversion: A next-generation seismic velocity model building method F Yang, J Ma Geophysics 84 (4), R583-R599, 2019 | 447 | 2019 |
Velocity model building with a modified fully convolutional network W Wang, F Yang, J Ma SEG International Exposition and Annual Meeting, SEG-2018-2997566, 2018 | 82 | 2018 |
Robust phase unwrapping via deep image prior for quantitative phase imaging F Yang, TA Pham, N Brandenberg, MP Lütolf, J Ma, M Unser IEEE Transactions on Image Processing 30, 7025-7037, 2021 | 42 | 2021 |
Automatic salt detection with machine learning W Wang, F Yang, J Ma 80th EAGE Conference and Exhibition 2018 2018 (1), 1-5, 2018 | 33 | 2018 |
Deep-learning projector for optical diffraction tomography F Yang, T Pham, H Gupta, M Unser, J Ma Optics express 28 (3), 3905-3921, 2020 | 31 | 2020 |
FWIGAN: Full‐Waveform Inversion via a Physics‐Informed Generative Adversarial Network F Yang, J Ma Journal of Geophysical Research: Solid Earth 128 (4), e2022JB025493, 2023 | 20* | 2023 |
Seismic random noise attenuation via self-supervised transfer learning H Sun, F Yang, J Ma IEEE geoscience and remote sensing letters 19, 1-5, 2022 | 19 | 2022 |
Quantitative reconstruction of defects in multi-layered bonded composites using fully convolutional network-based ultrasonic inversion J Rao, F Yang, H Mo, S Kollmannsberger, E Rank Journal of Sound and Vibration 542, 117418, 2023 | 14 | 2023 |
Wasserstein distance-based full-waveform inversion with a regularizer powered by learned gradient F Yang, J Ma IEEE Transactions on Geoscience and Remote Sensing 61, 1-13, 2023 | 3 | 2023 |
Full-waveform Inversion Using A Learned Regularization P Sun, F Yang, H Liang, J Ma IEEE Transactions on Geoscience and Remote Sensing, 2023 | 1 | 2023 |
14 Regularizing Neural Network for Phase Unwrapping T Pham, F Yang, M Unser | | |