144 citations · 145 across the 2 of their papers we have counts for
4 papers · 1 filter
Data-driven Accelerogram Synthesis using Deep Generative Models
Manuel A. Florez, Michaelangelo Caporale, Pakpoom Buabthong +3
Robust estimation of ground motions generated by scenario earthquakes is critical for many engineering applications. We leverage recent advances in Generative Adversarial Networks…
Reliable Real-time Seismic Signal/Noise Discrimination with Machine Learning
Men-Andrin Meier, Zachary E. Ross, Anshul Ramachandran +7
In Earthquake Early Warning (EEW), every sufficiently impulsive signal is potentially the first evidence for an unfolding large earthquake. More often than not, however, impulsive…
Generalized Seismic Phase Detection with Deep Learning
Zachary E. Ross, Men-Andrin Meier, Egill Hauksson +1
To optimally monitor earthquake-generating processes, seismologists have sought to lower detection sensitivities ever since instrumental seismic networks were started about a centu…
P-wave arrival picking and first-motion polarity determination with deep learning
Zachary E. Ross, Men-Andrin Meier, Egill Hauksson
Determining earthquake hypocenters and focal mechanisms requires precisely measured P-wave arrival times and first-motion polarities. Automated algorithms for estimating these quan…