5 citations · 5 across the 1 of their papers we have counts for
5 papers
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…
Distributed Machine Learning for Computational Engineering using MPI
Kailai Xu, Weiqiang Zhu, Eric Darve
We propose a framework for training neural networks that are coupled with partial differential equations (PDEs) in a parallel computing environment. Unlike most distributed computi…
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…
CRED: A Deep Residual Network of Convolutional and Recurrent Units for Earthquake Signal Detection
S. Mostafa Mousavi, Weiqiang Zhu, Yixiao Sheng +1
Earthquake signal detection is at the core of observational seismology. A good detection algorithm should be sensitive to small and weak events with a variety of waveform shapes, r…