| PhaseNet: a deep-neural-network-based seismic arrival-time picking method W Zhu, GC Beroza Geophysical Journal International 216 (1), 261-273, 2018 | 1199 | 2018 |
| Earthquake transformer—an attentive deep-learning model for simultaneous earthquake detection and phase picking SM Mousavi, WL Ellsworth, W Zhu, LY Chuang, GC Beroza Nature communications 11 (1), 3952, 2020 | 873 | 2020 |
| Seismic signal denoising and decomposition using deep neural networks W Zhu, SM Mousavi, GC Beroza IEEE Transactions on Geoscience and Remote Sensing 57 (11), 9476-9488, 2019 | 459 | 2019 |
| CRED: A deep residual network of convolutional and recurrent units for earthquake signal detection SM Mousavi, W Zhu, Y Sheng, GC Beroza Scientific reports 9 (1), 10267, 2019 | 376 | 2019 |
| STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI SM Mousavi, Y Sheng, W Zhu, GC Beroza IEEE Access 7, 179464-179476, 2019 | 371 | 2019 |
| Unsupervised clustering of seismic signals using deep convolutional autoencoders SM Mousavi, W Zhu, W Ellsworth, G Beroza IEEE Geoscience and Remote Sensing Letters 16 (11), 1693-1697, 2019 | 191 | 2019 |
| Rapid characterization of the July 2019 Ridgecrest, California, earthquake sequence from raw seismic data using machine‐learning phase picker M Liu, M Zhang, W Zhu, WL Ellsworth, H Li Geophysical Research Letters 47 (4), e2019GL086189, 2020 | 168 | 2020 |
| Using a deep neural network and transfer learning to bridge scales for seismic phase picking C Chai, M Maceira, HJ Santos‐Villalobos, SV Venkatakrishnan, ... Geophysical Research Letters 47 (16), e2020GL088651, 2020 | 134 | 2020 |
| Machine‐Learning‐Based High‐Resolution Earthquake Catalog Reveals How Complex Fault Structures Were Activated during the 2016–2017 Central Italy Sequence YJ Tan, F Waldhauser, WL Ellsworth, M Zhang, W Zhu, M Michele, ... The Seismic Record 1 (1), 11-19, 2021 | 131 | 2021 |
| Fault valving and pore pressure evolution in simulations of earthquake sequences and aseismic slip W Zhu, KL Allison, EM Dunham, Y Yang Nature communications 11 (1), 4833, 2020 | 128 | 2020 |
| Earthquake phase association using a Bayesian Gaussian mixture model W Zhu, IW McBrearty, SM Mousavi, WL Ellsworth, GC Beroza Journal of Geophysical Research: Solid Earth 127 (5), e2021JB023249, 2022 | 120 | 2022 |
| LOC‐FLOW: An End‐to‐End Machine Learning‐Based High‐Precision Earthquake Location Workflow M Zhang, M Liu, T Feng, R Wang, W Zhu Seismological Research Letters, 2022 | 120 | 2022 |
| Integrating deep neural networks with full-waveform inversion: Reparameterization, regularization, and uncertainty quantification W Zhu, K Xu, E Darve, B Biondi, GC Beroza Geophysics 87 (1), R93-R109, 2022 | 89 | 2022 |
| A general approach to seismic inversion with automatic differentiation W Zhu, K Xu, E Darve, GC Beroza Computers & Geosciences 151, 104751, 2021 | 81 | 2021 |
| Machine‐learning‐based analysis of the Guy‐Greenbrier, Arkansas earthquakes: A tale of two sequences Y Park, SM Mousavi, W Zhu, WL Ellsworth, GC Beroza Geophysical Research Letters 47 (6), e2020GL087032, 2020 | 80 | 2020 |
| The magmatic web beneath Hawai ‘i JD Wilding, W Zhu, ZE Ross, JM Jackson Science 379 (6631), 462-468, 2023 | 74 | 2023 |
| An End‐To‐End Earthquake Detection Method for Joint Phase Picking and Association Using Deep Learning W Zhu, KS Tai, SM Mousavi, P Bailis, GC Beroza Journal of Geophysical Research: Solid Earth 127 (3), e2021JB023283, 2022 | 73 | 2022 |
| Seismic signal augmentation to improve generalization of deep neural networks W Zhu, SM Mousavi, GC Beroza Advances in Geophysics 61, 151-177, 2020 | 68 | 2020 |
| QuakeFlow: a scalable machine-learning-based earthquake monitoring workflow with cloud computing W Zhu, AB Hou, R Yang, A Datta, SM Mousavi, WL Ellsworth, GC Beroza Geophysical Journal International, 2022 | 63 | 2022 |
| Seismic arrival-time picking on distributed acoustic sensing data using semi-supervised learning W Zhu, E Biondi, J Li, J Yin, ZE Ross, Z Zhan Nature Communications 14 (1), 8192, 2023 | 52 | 2023 |
Greg BerozaDepartment of Geophysics, Stanford University在 stanford.edu 的電子郵件地址已通過驗證