Zobrazeno 1 - 10
of 197
pro vyhledávání: '"Rümpker, Georg"'
In this study, we present an artificial neural network (ANN)-based approach for travel-time tomography of a volcanic edifice. We employ ray tracing to simulate the propagation of seismic waves through the heterogeneous medium of a volcanic edifice, a
Externí odkaz:
http://arxiv.org/abs/2311.01794
Autor:
Li, Wei, Chakraborty, Megha, Cartaya, Claudia Quinteros, koehler, Jonas, Faber, Johannes, Ruempker, Georg, Srivastava, Nishtha
Seismology has witnessed significant advancements in recent years with the application of deep learning methods to address a broad range of problems. These techniques have demonstrated their remarkable ability to effectively extract statistical prope
Externí odkaz:
http://arxiv.org/abs/2308.11428
Reliable earthquake forecasting methods have long been sought after, and so the rise of modern data science techniques raises a new question: does deep learning have the potential to learn this pattern? In this study, we leverage the large amount of
Externí odkaz:
http://arxiv.org/abs/2307.01812
Autor:
Li, Wei, Koehler, Jonas, Chakraborty, Megha, Quinteros-Cartaya, Claudia, Ruempker, Georg, Srivastava, Nishtha
Seismic phase picking and magnitude estimation are essential components of real time earthquake monitoring and earthquake early warning systems. Reliable phase picking enables the timely detection of seismic wave arrivals, facilitating rapid earthqua
Externí odkaz:
http://arxiv.org/abs/2211.09539
Autor:
Chakraborty, Megha, Fenner, Darius, Li, Wei, Faber, Johannes, Zhou, Kai, Ruempker, Georg, Stoecker, Horst, Srivastava, Nishtha
The detection and rapid characterisation of earthquake parameters such as magnitude are of prime importance in seismology, particularly in applications such as Earthquake Early Warning (EEW). Traditionally, algorithms such as STA/LTA are used for eve
Externí odkaz:
http://arxiv.org/abs/2204.02924
Autor:
Li, Wei, Sha, Yu, Zhou, Kai, Faber, Johannes, Ruempker, Georg, Stoecker, Horst, Srivastava, Nishtha
Reliable earthquake detection and seismic phase classification is often challenging especially in the circumstances of low magnitude events or poor signal-to-noise ratio. With improved seismometers and better global coverage, a sharp increase in the
Externí odkaz:
http://arxiv.org/abs/2204.02870
Autor:
Li, Wei, Chakraborty, Megha, Cartaya, Claudia Quinteros, Köhler, Jonas, Faber, Johannes, Meier, Men-Andrin, Rümpker, Georg, Srivastava, Nishtha
Publikováno v:
In Computers and Geosciences October 2024 192
Autor:
Chakraborty, Megha, Li, Wei, Faber, Johannes, Ruempker, Georg, Stoecker, Horst, Srivastava, Nishtha
The rapid characterisation of earthquake parameters such as its magnitude is at the heart of Earthquake Early Warning (EEW). In traditional EEW methods the robustness in the estimation of earthquake parameters have been observed to increase with the
Externí odkaz:
http://arxiv.org/abs/2112.07551
Publikováno v:
In Artificial Intelligence in Geosciences December 2024 5
Autor:
Fenner, Darius, Ruempker, Georg, Li, Wei, Chakraborty, Megha, Faber, Johannes, Koehler, Jonas, Stoecker, Horst, Srivastava, Nishtha
Many active volcanoes in the world exhibit Strombolian activity, which is typically characterized by relatively frequent mild events and also by rare and much more destructive major explosions and paroxysms. Detailed analyses of past major and minor
Externí odkaz:
http://arxiv.org/abs/2111.01513