A Parallel Algorithm for Studying the Ice Cover Impact onto Seismic Waves Propagation in the Shallow Arctic Waters

  • Galina ReshetovaEmail author
  • Vladimir Cheverda
  • Vadim Lisitsa
  • Valery Khaidykov
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 965)


The seismic study in the Arctic transition zones in the summer season is troublesome because of the presence of large areas covered by shallow waters like bays, lakes, rivers, their estuaries and so on. The winter season is more convenient and essentially facilitates logistic operations and implementation of seismic acquisition. However in the winter there is another complicating factor: intensive seismic noise generated by sources installed on the floating ice. To understand peculiarities of seismic waves and the origin of such an intensive noise, a representative series of numerical experiments has been performed. In order to simulate the interaction of seismic waves with irregular perturbations of underside of the ice cover, a finite-difference technique based on locally refined in time and in space grids is used. The need to use such grids is primarily due to the different scales of heterogeneities in a reference medium and the ice cover should be taken into account. We use the domain decomposition method to separate the elastic/viscoelastic model into subdomains with different scales. Computations for each subdomain are carried out in parallel. The data exchange between the two groups of CPU is done simultaneously by coupling a coarse and a fine grids. The results of the numerical experiments prove that the main impact to noise is multiple conversions of flexural waves to the body ones and vice versa and open the ways to reduce this noise.


Seismic waves Transition zones Finite-difference schemes Local grid refinement Domain decomposition 



This work was supported by RSF (project No. 17-17-01128). The research was carried out using the equipment of the shared research facilities of HPC computing resources at Lomonosov Moscow State University, Joint Supercomputer Center of RAS and the Siberian Supercomputer Center.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Galina Reshetova
    • 1
    • 2
    Email author
  • Vladimir Cheverda
    • 2
  • Vadim Lisitsa
    • 2
  • Valery Khaidykov
    • 2
  1. 1.Institute of Computational Mathematics and Mathematical Geophysics SB RASNovosibirskRussia
  2. 2.Trofimuk Institute of Petroleum Geology and Geophysics SB RASNovosibirskRussia

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