153 citations · 588 across the 9 of their papers we have counts for
6 papers · 1 filter
Earthquake Phase Association with Graph Neural Networks
Ian W. McBrearty, Gregory C. Beroza
Seismic phase association connects earthquake arrival time measurements to their causative sources. Effective association must determine the number of discrete events, their locati…
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…
Earthquake Location and Magnitude Estimation with Graph Neural Networks
Ian W. McBrearty, Gregory C. Beroza
We solve the traditional problems of earthquake location and magnitude estimation through a supervised learning approach, where we train a Graph Neural Network to predict estimates…
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…
Bayesian-Deep-Learning Estimation of Earthquake Location from Single-Station Observations
S. Mostafa Mousavi, Gregory C. Beroza
We present a deep learning method for single-station earthquake location, which we approach as a regression problem using two separate Bayesian neural networks. We use a multi-task…
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…