Visualizing the Seismic Wavefield with AlpArray

Author(s):  
On Ki Angel Ling ◽  
Simon Stähler ◽  
Domenico Giardini ◽  
the AlpArray Working Group

<p>The AlpArray Seismic Network (AASN) is a large-scale multidisciplinary seismic network in Europe that consists of over 600 3-component (3C) broadband stations with mean inter-station distance of 30-40km. This dense array allows the recording of the seismic wave propagation of distant earthquakes at a resolution of typical body and surface waves.</p><p>By animating the spatially-dense seismic recordings of the AASN, we can visualize seismic waves propagating across the European Alps as a function of space and time. Our 3C ground motion animations illustrate the full spatial-temporal evolution of global body and surface waves and demonstrates how a dense array allows the transformation from translation measurements at single stations to spatial gradients of the wavefield at the surface, capturing both small- and large-scale wave propagation phenomena. The addition of travel-time estimation, ray path illustration, and array-specific information such as slowness vector of incoming waves facilitate identification of seismic phases and their arrival-angle deviations. We will highlight some interesting observations of different seismic wave types in the animations of a few example teleseismic events during the course of the AASN between 2016-2019. Application for future research and education will also be discussed.</p>

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Stephan Fischer ◽  
Marc Dinh ◽  
Vincent Henry ◽  
Philippe Robert ◽  
Anne Goelzer ◽  
...  

AbstractDetailed whole-cell modeling requires an integration of heterogeneous cell processes having different modeling formalisms, for which whole-cell simulation could remain tractable. Here, we introduce BiPSim, an open-source stochastic simulator of template-based polymerization processes, such as replication, transcription and translation. BiPSim combines an efficient abstract representation of reactions and a constant-time implementation of the Gillespie’s Stochastic Simulation Algorithm (SSA) with respect to reactions, which makes it highly efficient to simulate large-scale polymerization processes stochastically. Moreover, multi-level descriptions of polymerization processes can be handled simultaneously, allowing the user to tune a trade-off between simulation speed and model granularity. We evaluated the performance of BiPSim by simulating genome-wide gene expression in bacteria for multiple levels of granularity. Finally, since no cell-type specific information is hard-coded in the simulator, models can easily be adapted to other organismal species. We expect that BiPSim should open new perspectives for the genome-wide simulation of stochastic phenomena in biology.


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