4 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…
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…
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…
Locality-Sensitive Hashing for Earthquake Detection: A Case Study of Scaling Data-Driven Science
Kexin Rong, Clara E. Yoon, Karianne J. Bergen +4
In this work, we report on a novel application of Locality Sensitive Hashing (LSH) to seismic data at scale. Based on the high waveform similarity between reoccurring earthquakes,…