Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Münchmeyer, Jannes"'
Autor:
Liu, Tianlin, Münchmeyer, Jannes, Laurenti, Laura, Marone, Chris, de Hoop, Maarten V., Dokmanić, Ivan
We introduce the Seismic Language Model (SeisLM), a foundational model designed to analyze seismic waveforms -- signals generated by Earth's vibrations such as the ones originating from earthquakes. SeisLM is pretrained on a large collection of open-
Externí odkaz:
http://arxiv.org/abs/2410.15765
Autor:
Münchmeyer, Jannes, Giffard-Roisin, Sophie, Malfante, Marielle, Frank, William, Poli, Piero, Marsan, David, Socquet, Anne
Documenting the interplay between slow deformation and seismic ruptures is essential to understand the physics of earthquakes nucleation. However, slow deformation is often difficult to detect and characterize. The most pervasive seismic markers of s
Externí odkaz:
http://arxiv.org/abs/2311.13971
Autor:
Münchmeyer, Jannes
Seismic phase association is an essential task for characterising seismicity: given a collection of phase picks, identify all seismic events in the data. In recent years, machine learning pickers have lead to a rapid growth in the number of seismic p
Externí odkaz:
http://arxiv.org/abs/2310.11157
Autor:
Münchmeyer, Jannes
Erdbeben gehören zu den zerstörerischsten Naturgefahren auf diesem Planeten. Obwohl Erdbeben seit Jahrtausenden dokumentiert sing, bleiben viele Fragen zu Erdbeben unbeantwortet. Eine Frage ist die Vorhersagbarkeit von Brüchen: Inwieweit ist es m
Externí odkaz:
http://edoc.hu-berlin.de/18452/26129
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
Ruptures of the largest earthquakes can last between a few seconds and several minutes. An early assessment of the final earthquake size is essential for early warning systems. However, it is still unclear when in the rupture history this final size
Externí odkaz:
http://arxiv.org/abs/2203.08622
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
Precise real time estimates of earthquake magnitude and location are essential for early warning and rapid response. While recently multiple deep learning approaches for fast assessment of earthquakes have been proposed, they usually rely on either s
Externí odkaz:
http://arxiv.org/abs/2101.02010
Earthquakes are major hazards to humans, buildings and infrastructure. Early warning methods aim to provide advance notice of incoming strong shaking to enable preventive action and mitigate seismic risk. Their usefulness depends on accuracy, the rel
Externí odkaz:
http://arxiv.org/abs/2009.06316