144 citations · 145 across the 2 of their papers we have counts for
5 papers
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…
PhaseLink: A Deep Learning Approach to Seismic Phase Association
Zachary E. Ross, Yisong Yue, Men-Andrin Meier +2
Seismic phase association is a fundamental task in seismology that pertains to linking together phase detections on different sensors that originate from a common earthquake. It is…
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…