Many geophysical inverse problems involve large and dense coefficient matrices that often require an immense amount of computing power. Some methods can be used to reduce the processing time or physical memory required. This paper pose a significant challenge to solve large-scale inverse problems. We have developed a method that combines the adaptive mesh discretization and sparse mesh to reduce the computational complexity of CSEM inverse problems. The sparse mesh is created by Delaunay Triangulations method and constrained by seismic image. The nodes for generating triangular mesh are extracted from seismic coherence map. This sparse mesh is including all the information of geological features which are extracted from seismic image. A synthetic CSEM data are simulated for sparse mesh testing. As a result, the seismic coherence driven sparse mesh has an as high resolution inversion result as normal unstructured triangular dense mesh. Comparing with the same number unstructured triangular sparse mesh, seismic coherence driven sparse mesh has an advantage of vertical resolution.


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  1. Ando. S.
    [2000] Image field categorization and edge/corner detection from gradient covariance, Pattern Analysis and Machine Intelligence. IEEE Transactions on, 22(2), 179–190.
    [Google Scholar]
  2. Hale. D.
    [2002] Atomic meshes: from seismic imaging to reservoir simulation. Proceedings of the 8thEuropean Conference on the Mathematics of Oil Recovery, Citeseer.
    [Google Scholar]
  3. Hale. D. and Emanuel. J.
    [2003] Atomic meshing of seismic images. 65th EAGE Conference & Exhibition.
    [Google Scholar]
  4. Kothe. U.
    [2003] Integrated edge and junction detection with the boundary tensor, in Computer Vision, 2003. Proceedings. Ninth IEEE International Conference on, 424–431, IEEE.
    [Google Scholar]
  5. Mike. B. and Steve. F.
    [1995] 3-D seismic discontinuity for faults and stratigraphic features: The coherence cube. The Leading Edge. 1053–1058.
    [Google Scholar]
  6. Mokhtarian, F and Mohanna, F.
    [2006] Performance evaluation of corner detectors using consistency and accuracy measures. Computer Vision and Image Understanding, 102(1), 81–94.
    [Google Scholar]
  7. Rebay. S.
    [1993] Efficient unstructured mesh generation by means of Delaunay Triangulation and Bowyer-Watson algorithm. Journal of computational physics. 106, 125–138.
    [Google Scholar]
  8. RuneMittet and J.P.Morten.
    [2013] The marine controlled-source electromagnetic method in shallow water, Geophysics, 78(2), E67–E77, 11 FIGS.
    [Google Scholar]
  9. Van de Weijer. J. and Gevers. T.
    [2004] Tensor based feature detection for color images. Color and Imaging Conference, 2004, 100–105, Society for Imaging Science and Technology.
    [Google Scholar]
  10. Yu, Z., Holst, M.J., Cheng, Y. and McCammon, J.A.
    [2008] Feature-preserving adaptive mesh generation for molecular shape modeling and simulation. Journal of Molecular Graphics and Modelling, 26(8), 1370–1380.
    [Google Scholar]

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