Articles avec mandats d'accès public - Weiqiang ZhuEn savoir plus
Non disponibles : 6
DeepShake: Shaking Intensity Prediction Using Deep Spatiotemporal RNNs for Earthquake Early Warning
A Datta, DJ Wu, W Zhu, M Cai, WL Ellsworth
Seismological Society of America 93 (3), 1636-1649, 2022
Exigences : US Department of Energy
Deep neural networks design and analysis for automatic phase pickers from three-component microseismic recordings
J Zheng, S Shen, T Jiang, W Zhu
Geophysical Journal International 220 (1), 323-334, 2020
Exigences : National Natural Science Foundation of China
Three-dimensional reverse time migration of ground-penetrating radar signals
W Zhu, Q Huang, L Liu, B Ma
Pure and Applied Geophysics 177 (2), 853-865, 2020
Exigences : National Natural Science Foundation of China
Imaging shallow fault structures by three-dimensional reverse time migration of ground penetration radar data
B Ma, W Zhu, Q Huang
Journal of Applied Geophysics 190, 104342, 2021
Exigences : National Natural Science Foundation of China
Application of reverse time migration on GPR data for detecting internal structures in a sand dune
W Zhu*, Q Huang, L Liu
SEG Technical Program Expanded Abstracts 2015, 2269-2274, 2015
Exigences : National Natural Science Foundation of China
Learning generative neural networks with physics knowledge
K Xu, W Zhu, E Darve
Research in the Mathematical Sciences 9 (2), 33, 2022
Exigences : US Department of Energy
Disponibles quelque part : 23
PhaseNet: a deep-neural-network-based seismic arrival-time picking method
W Zhu, GC Beroza
Geophysical Journal International 216 (1), 261-273, 2018
Exigences : US National Science Foundation
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
Exigences : US Department of Defense
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
Exigences : US National Science Foundation
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
Exigences : US National Science Foundation
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
Exigences : US National Science Foundation, US Department of Defense
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
Exigences : Conseil de recherches en sciences naturelles et en génie du Canada, National …
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
Exigences : US Department of Energy
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
Exigences : US National Science Foundation, US Department of Energy, UK Natural …
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
Exigences : US National Science Foundation
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
Exigences : US Department of Energy
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
Exigences : Conseil de recherches en sciences naturelles et en génie du Canada
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
Exigences : US Department of Energy
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
Exigences : US Department of Energy
A general approach to seismic inversion with automatic differentiation
W Zhu, K Xu, E Darve, GC Beroza
Computers & Geosciences 151, 104751, 2021
Exigences : US Department of Energy
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