Zobrazeno 1 - 10
of 1 722
pro vyhledávání: '"A. Rietbrock"'
Autor:
T. Bornstein, D. Lange, J. Münchmeyer, J. Woollam, A. Rietbrock, G. Barcheck, I. Grevemeyer, F. Tilmann
Publikováno v:
Earth and Space Science, Vol 11, Iss 1, Pp n/a-n/a (2024)
Abstract 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
Externí odkaz:
https://doaj.org/article/218a0c35c056486295b967f15c8408bd
Publikováno v:
Geophysical Research Letters, Vol 50, Iss 11, Pp n/a-n/a (2023)
Abstract On June 21st, a Mw6.2 earthquake struck the Afghan‐Pakistan‐border‐region, situated within the India‐Asia collision. Thousand thirty‐nine deaths were reported, making the earthquake the deadliest of 2022. We investigate the event's
Externí odkaz:
https://doaj.org/article/98039d838cec41709007e0600974c4b9
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
Autor:
D. Schlaphorst, N. Harmon, J. M. Kendall, C. A. Rychert, J. Collier, A. Rietbrock, S. Goes, the VoiLA Team
Publikováno v:
Geochemistry, Geophysics, Geosystems, Vol 22, Iss 7, Pp n/a-n/a (2021)
Abstract The crust and upper mantle structure of the Greater and Lesser Antilles Arc provides insights into key subduction zone processes in a unique region of slow convergence of old slow‐spreading oceanic lithosphere. We use ambient noise tomogra
Externí odkaz:
https://doaj.org/article/406ca2d6d6ec4811a6950f51caf7c255
Publikováno v:
Solid Earth, Vol 9, Pp 1035-1049 (2018)
The Sumatran subduction zone exhibits strong seismic and tsunamogenic potential with the prominent examples of the 2004, 2005 and 2007 earthquakes. Here, we invert travel-time data of local earthquakes for vp and vp∕vs velocity models of the cen
Externí odkaz:
https://doaj.org/article/1675f1477d8542b59295aedfe55d3487
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
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
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
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
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