Zobrazeno 1 - 10
of 1 723
pro vyhledávání: '"Rietbrock, A."'
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:
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
Publikováno v:
Geophysical Research Letters, Vol 51, Iss 11, Pp n/a-n/a (2024)
Abstract Earthquakes that rupture several faults occur frequently within the shallow lithosphere but are rarely observed for intermediate‐depth events (70–300 km). On 29 November 2007, the Mw7.4 Martinique earthquake struck the Lesser Antilles Is
Externí odkaz:
https://doaj.org/article/0e79947892854142b53a2e4db4c8e44f
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
Autor:
Sergio Leon‐Rios, Valentina Reyes‐Wagner, Daniela Calle‐Gardella, Andreas Rietbrock, Steven Roecker, Andrei Maksymowicz, Diana Comte
Publikováno v:
Geochemistry, Geophysics, Geosystems, Vol 25, Iss 5, Pp n/a-n/a (2024)
Abstract Recordings of earthquakes by a temporary deployment of 84 short period seismometers in northern Chile were used to derive regional 3D seismic velocity models for the Taltal segment. We used the Regressive ESTimator (REST) package for event d
Externí odkaz:
https://doaj.org/article/ae202891dd244ff981699f95ac05e2ad
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
Autor:
Comte, Diana, Farías, Marcelo, Calle-Gardella, Daniela, Navarro-Aranguiz, Andrea, Roecker, Steven, Rietbrock, Andreas
Publikováno v:
In Tectonophysics 5 January 2023 846