523 citations · 1.3k across the 14 of their papers we have counts for
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Earthquake Aftershock Forecasting using Conditional Generative Models
Weiqiang Zhu
Forecasting how aftershocks evolve in space and time after a large earthquake is a central problem in statistical seismology and underpins operational earthquake forecasting. Exist…
Phase Neural Operator for Multi-Station Picking of Seismic Arrivals
Hongyu Sun, Zachary E. Ross, Weiqiang Zhu +1
Seismic wave arrival time measurements form the basis for numerous downstream applications. State-of-the-art approaches for phase picking use deep neural networks to annotate seism…
Seismic Arrival-time Picking on Distributed Acoustic Sensing Data using Semi-supervised Learning
Weiqiang Zhu, Ettore Biondi, Jiaxuan Li +3
Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake monitoring and subsurface imaging. The recorded seismic signals by DAS have several distinct characteris…
Neural mixture model association of seismic phases
Zachary E. Ross, Weiqiang Zhu, Kamyar Azizzadenesheli
Seismic phase association is the task of grouping phase arrival picks across a seismic network into subsets with common origins. Building on recent successes in this area with mach…
QuakeFlow: A Scalable Machine-learning-based Earthquake Monitoring Workflow with Cloud Computing
Weiqiang Zhu, Alvin Brian Hou, Robert Yang +4
Earthquake monitoring workflows are designed to detect earthquake signals and to determine source characteristics from continuous waveform data. Recent developments in deep learnin…
An End-to-End Earthquake Detection Method for Joint Phase Picking and Association using Deep Learning
Weiqiang Zhu, Kai Sheng Tai, S. Mostafa Mousavi +2
Earthquake monitoring by seismic networks typically involves a workflow consisting of phase detection/picking, association, and location tasks. In recent years, the accuracy of the…
Earthquake Phase Association using a Bayesian Gaussian Mixture Model
Weiqiang Zhu, Ian W. McBrearty, S. Mostafa Mousavi +2
Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual earthquakes and play an important role in earthquake monitori…
Integrating Deep Neural Networks with Full-waveform Inversion: Reparametrization, Regularization, and Uncertainty Quantification
Weiqiang Zhu, Kailai Xu, Eric Darve +2
Full-waveform inversion (FWI) is an accurate imaging approach for modeling velocity structure by minimizing the misfit between recorded and predicted seismic waveforms. However, th…
Fault Valving and Pore Pressure Evolution in Simulations of Earthquake Sequences and Aseismic Slip
Weiqiang Zhu, Kali L. Allison, Eric M. Dunham +1
Fault-zone fluids control effective normal stress and fault strength. While most earthquake models assume a fixed pore fluid pressure distribution, geologists have documented fault…
Seismic Signal Denoising and Decomposition Using Deep Neural Networks
Weiqiang Zhu, S. Mostafa Mousavi, Gregory C. Beroza
Denoising and filtering are widely used in routine seismic-data-processing to improve the signal-to-noise ratio (SNR) of recorded signals and by doing so to improve subsequent anal…
PhaseNet: A Deep-Neural-Network-Based Seismic Arrival Time Picking Method
Weiqiang Zhu, Gregory C. Beroza
As the number of seismic sensors grows, it is becoming increasingly difficult for analysts to pick seismic phases manually and comprehensively, yet such efforts are fundamental to…