13 citations · 14 across the 3 of their papers we have counts for
5 papers · 1 filter
PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning
Thomas Bornstein, Dietrich Lange, Jannes Münchmeyer +5
Detecting phase arrivals and pinpointing the arrival times of seismic phases in seismograms is crucial for many seismological analysis workflows. For land station data machine lear…
Bayesian regional moment tensor from ocean bottom seismograms recorded in the Lesser Antilles: Implications for regional stress field
Mike Lindner, Andreas Rietbrock, Lidong Bie +7
In this paper, we perform full-waveform regional moment tensor (RMT) inversions, to gain insight into the stress distribution along the Lesser Antilles arc. We developed a novel in…
Machine learning event detection workflows in practice: A case study from the 2019 Durrës aftershock sequence
Jack Woollam, Vincent Van der Heiden, Andreas Rietbrock +3
Machine Learning (ML) methods have demonstrated exceptional performance in recent years when applied to the task of seismic event detection. With numerous ML techniques now availab…
SeisBench -- A Toolbox for Machine Learning in Seismology
Jack Woollam, Jannes Münchmeyer, Frederik Tilmann +10
Machine Learning (ML) methods have seen widespread adoption in seismology in recent years. The ability of these techniques to efficiently infer the statistical properties of large…
Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers
Jannes Münchmeyer, Jack Woollam, Andreas Rietbrock +10
Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significant…