213 citations · 336 across the 4 of their papers we have counts for
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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…
Transfer learning: Improving neural network based prediction of earthquake ground shaking for an area with insufficient training data
Dario Jozinović, Anthony Lomax, Ivan Štajduhar +1
In a recent study (Jozinović et al, 2020) we showed that convolutional neural networks (CNNs) applied to network seismic traces can be used for rapid prediction of earthquake peak…
Local earthquakes detection: A benchmark dataset of 3-component seismograms built on a global scale
Fabrizio Magrini, Dario Jozinović, Fabio Cammarano +2
Machine learning is becoming increasingly important in scientific and technological progress, due to its ability to create models that describe complex data and generalize well. Th…