Výsledky vyhledávání - "Rietbrock Andreas"

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Calibration of the strain amplitude recorded with DAS using a strainmeter array

Autor: Forbriger, Thomas, Karamzadeh, Nasim, Azzola, Jérôme, Gaucher, Emmanuel, Widmer-Schnidrig, Rudolf, Rietbrock, Andreas

The power of distributed acoustic sensing (DAS) lies in its ability to sample deformation signals along an optical fiber at hundreds of locations with only one interrogation unit (IU). While the IU is calibrated to record 'fiber strain', the properti

Externí odkaz: http://arxiv.org/abs/2408.01151

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PickBlue: Seismic phase picking for ocean bottom seismometers with deep learning

Autor: Bornstein, Thomas, Lange, Dietrich, Münchmeyer, Jannes, Woollam, Jack, Rietbrock, Andreas, Barcheck, Grace, Grevemeyer, Ingo, Tilmann, Frederik

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 learning methods have already found widespread adoption. However, deep lea

Externí odkaz: http://arxiv.org/abs/2304.06635

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Bayesian regional moment tensor from ocean bottom seismograms recorded in the Lesser Antilles: Implications for regional stress field

Autor: Lindner, Mike, Rietbrock, Andreas, Bie, Lidong, Goes, Saskia, Collier, Jenny, Rychert, Catherine, Harmon, Nicholas, Hicks, Stephen P., Henstock, Tim, group, the VoiLA working

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 inversion approach, AmPhiB - Amphibious Bayesian, taking into account un

Externí odkaz: http://arxiv.org/abs/2206.05502

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Machine learning event detection workflows in practice: A case study from the 2019 Durr\'es aftershock sequence

Autor: Woollam, Jack, Van der Heiden, Vincent, Rietbrock, Andreas, Schurr, Bernd, Tilmann, Frederik, Dushi, Edmond

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 available for detecting seismicity, applying these methods in practice can he

Externí odkaz: http://arxiv.org/abs/2205.12033

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SeisBench -- A Toolbox for Machine Learning in Seismology

Autor: Woollam, Jack, Münchmeyer, Jannes, Tilmann, Frederik, Rietbrock, Andreas, Lange, Dietrich, Bornstein, Thomas, Diehl, Tobias, Giunchi, Carlo, Haslinger, Florian, Jozinović, Dario, Michelini, Alberto, Saul, Joachim, Soto, Hugo

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 datasets often provides significant improvements over traditional tech

Externí odkaz: http://arxiv.org/abs/2111.00786

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Which picker fits my data? A quantitative evaluation of deep learning based seismic pickers

Autor: Münchmeyer, Jannes, Woollam, Jack, Rietbrock, Andreas, Tilmann, Frederik, Lange, Dietrich, Bornstein, Thomas, Diehl, Tobias, Giunchi, Carlo, Haslinger, Florian, Jozinović, Dario, Michelini, Alberto, Saul, Joachim, Soto, Hugo

Seismic event detection and phase picking are the base of many seismological workflows. In recent years, several publications demonstrated that deep learning approaches significantly outperform classical approaches and even achieve human-like perform

Externí odkaz: http://arxiv.org/abs/2110.13671

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