With the computer power available today full 3D inversions of transient electromagnetic data (TEM) is no longer a dream of the past. Although it is possible to perform these inversions, the problems still scale in three dimensions making large datasets slow to invert. We here propose a new triple mesh method for inverting TEM datasets with multiple transmitters and multiple receivers per transmitter. The code is relative fast and with a manageable memory consumption. In this new approach we show that by using a decoupled regular structured model mesh and two finite element forward meshes, one with a coarse discretization and one with a fine discretization, we get a substantial speed up in calculations times without sacrificing much in terms of how well we fit the data. We show that we can invert large datasets by decomposing our domain and applying this triple mesh method on each domain separately.


Article metrics loading...

Loading full text...

Full text loading...


  1. Auken, E., Christiansen, A. V., Jacobsen, B. H., Foged, N., and Sørensen, K. I.
    , 2005, Piecewise 1D laterally constrained inversion of resistivity data: Geophysical Prospecting, 53, 497–506. doi:10.1111/j.1365‑2478.2005.00486.x
    https://doi.org/10.1111/j.1365-2478.2005.00486.x [Google Scholar]
  2. Auken, E., Christiansen, A. V., Kirkegaard, C., Fiandaca, G., Schamper, C., Behroozmand, A. A., Binley, A., Nielsen, E., Effersø, F., Christensen, N. B., Sørensen, K., Foged, N. & Vignoli, G.
    , 2015. An overview of a highly versatile forward and stable inverse algorithm for airborne, ground-based and borehole electromagnetic and electric data. Exploration Geophysics, 2015, 46, 223–235. http://dx.doi.org/10.1071/EG13097.
    [Google Scholar]
  3. Bezanson, J., Karpinski, S., Shah, V. B., & Edelman, A.
    (2012). Julia: A fast dynamic language for technical computing. arXiv preprint arXiv:1209.5145.
    [Google Scholar]
  4. Börner, R.-U., Ernst, O. G., & Spitzer, K.
    (2008). Fast 3-D simulation of transient electromagnetic fields by model reduction in the frequency domain using Krylov subspace projection. Geophysical Journal International, 173, 766–780.
    [Google Scholar]
  5. Cockett, R., Kang, S., Heagy, L. J., Pidlisecky, A., & Oldenburg, D. W.
    (2015). SimPEG: An open source framework for simulation and gradient based parameter estimation in geophysical applications. Computers & Geosciences, 85, 142–154.
    [Google Scholar]
  6. FiandacaG., AukenE., ChristiansenA. & KirkegaardC.
    , 2013. Voxel inversion of airborne EM data, Near Surface Geoscience 2013-19th EAGE European Meeting of Environmental and Engineering Geophysics, 1–4. 10.3997/2214‑4609.20131426.
    https://doi.org/10.3997/2214-4609.20131426 [Google Scholar]
  7. Jin, J.
    , 2014. Finite element method in electromagnetics, Third Edition, Wiley-IEEE Press.
    [Google Scholar]
  8. Menke, W.
    , 1989, Geophysical data analysis discrete inverse theory:Academic Press.
    [Google Scholar]
  9. Ruthotto, L., Treister, E., & Haber, E.
    (2016). jInv—A flexible Julia package for PDE parameter estimation. arXiv preprint arXiv:1606.07399.
    [Google Scholar]
  10. Um, E.S., Harris, J.M. and Alumbaugh, D.L.
    , 2010. 3D time-domain simulation of electromagnetic diffusion phenomena: A finite-element electric-field approach: Geophysics, 75(4), F115–F126.
    [Google Scholar]
  11. Ward, S.H., Hohmann, G.W.
    , 1988. Electromagnetic Theory for Geophysical Applications:, SEG.
    [Google Scholar]

Data & Media loading...

This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error