1887
2nd Australasian Exploration Geoscience Conference: Data to Discovery
  • ISSN: 2202-0586
  • E-ISSN:
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Abstract

Summary

A comprehensive understanding of the sources of uncertainty is essential in stochastic inversion workflows of magnetotelluric data. Input uncertainty related to the electromagnetic noise and measurement biases can be reliably estimated statistically during processing. Uncertainties related to limitations and oversimplifying assumptions made on the physics and geometry by the forward solver employed are usually lumped into error floors in magnetotelluric inversion workflows.

Here we propose a workflow for using 1D trans-dimensional Markov chain Monte Carlo samplers for estimating subsurface conductivity and its associated uncertainty. Our methodology replaces error floors with site-specific likelihood functions which are calculated using a machine learning algorithm trained on a set of synthetic 3D conductivity training images. The learning method quantitatively compensates for the bias caused by the 1D earth assumption. This is achieved by exploiting known dimensional properties of the magnetotelluric phase tensor.

We apply this workflow to synthetic data to quantify the improvement in reliability compared to classical 1D probabilistic inversion.

© 2019 Taylor and Francis Group LLC
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/content/journals/10.1080/22020586.2019.12073113
2019-12-01
2026-01-24

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References

  1. Bahr, K., 1991. Geological noise in magnetotelluric data: a classification of distortion types. Physics of the Earth and Planetary Interiors 66, 24–38.
  2. Bibby, H.M., Caldwell, T.G., Brown, C., 2005. Determinable and non-determinable parameters of galvanic distortion in magnetotellurics. Geophysical Journal International 163, 915–930.
  3. Bodin, T., Sambridge, M., Rawlinson, N., Arroucau, P., 2012. Transdimensional tomography with unknown data noise. Geophysical Journal International 189, 1536–1556.
  4. Brodie, R., Jiang, W., 2018. Trans-Dimensional Monte Carlo Inversion of Short Period Magnetotelluric Data for Cover Thickness Estimation. ASEG Extended Abstracts 2018, 1-7.
  5. Caldwell, T.G., Bibby, H.M., Brown, C., 2004. The magnetotelluric phase tensor. Geophysical Journal International 158, 457–469.
  6. Constable, S.C., Parker, R.L., Constable, C.G., 1987. Occam’s inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics 52, 289–300.
  7. Conway, D., Simpson, J., Didana, Y., Rugari, J., Heinson, G., 2018. Probabilistic Magnetotelluric Inversion with Adaptive Regularisation Using the No-U-Turns Sampler. Pure and Applied Geophysics 175, 2881–2894.
  8. Green, P.J., 1995, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination: Biometrika, 82(4), 711-732.
  9. Kelbert, A., Meqbel, N., Egbert, G.D., Tandon, K., 2014. ModEM: A modular system for inversion of electromagnetic geophysical data. Computers & Geosciences 66, 40–53.
  10. Ledo, J., 2005. 2-D Versus 3-D Magnetotelluric Data Interpretation. Surveys in Geophysics 26, 511–543.
  11. Mandolesi, E., Ogaya, X., Campanya, J., Piana Agostinetti, N., 2018. A reversible-jump Markov chain Monte Carlo algorithm for 1D inversion of magnetotelluric data. Computers & Geosciences 113, 94–105.
  12. Swift, C., 1967. A magnetotelluric investigation of an electric conductivity anomaly in the south-western. Ph.D. Thesis. MIT.
  13. Wait, J.R., 1962. Theory of magnetotelluric fields. Journal of Research of the National Bureau of Standards, Section D: Radio Propagation 66D, 509.
  14. Quinlan, J.R., 1992. Learning with continuous classes. In proceedings of the 5th Australian Joint Conference on Artificial Intelligence, 343-348.
  15. Weaver, J.T., Agarwal, A.K., Lilley, F.E.M., 2000. Characterization of the magnetotelluric tensor in terms of its invariants. Geophysical Journal International 141, 321–336.
  16. Xiang, E., Guo, R., Dosso, S.E., Liu, J., Dong, H., Ren, Z., 2018. Efficient hierarchical trans-dimensional Bayesian inversion of magnetotelluric data. Geophysical Journal International 213, 1751–1767.
/content/journals/10.1080/22020586.2019.12073113
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  • Article Type: Research Article
Keyword(s): machine learning; magnetotellurics; probabilistic inversion

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