Skip to main content
Log in

Transdimensional Bayesian inversion of time-domain airborne EM data

  • Published:
Applied Geophysics Aims and scope Submit manuscript

Abstract

To reduce the dependence of EM inversion on the choice of initial model and to obtain the global minimum, we apply transdimensional Bayesian inversion to time-domain airborne electromagnetic data. The transdimensional Bayesian inversion uses the Monte Carlo method to search the model space and yields models that simultaneously satisfy the acceptance probability and data fitting requirements. Finally, we obtain the probability distribution and uncertainty of the model parameters as well as the maximum probability. Because it is difficult to know the height of the transmitting source during flight, we consider a fixed and a variable flight height. Furthermore, we introduce weights into the prior probability density function of the resistivity and adjust the constraint strength in the inversion model by changing the weighing coefficients. This effectively solves the problem of unsatisfactory inversion results in the middle high-resistivity layer. We validate the proposed method by inverting synthetic data with 3% Gaussian noise and field survey data.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Agostinetti, N. P., and Malinverno, A., 2010, Receiver function inversion by trans-dimensional Monte Carlo sampling: Geophysical Journal International, 181(2), 858–872.

    Google Scholar 

  • Bodin, T., and Sambridge, M., 2009, Seismic tomography with the reversible jump algorithm: Geophysical Journal International, 178(3), 1411–1436.

    Article  Google Scholar 

  • Brodie, R. C., and Sambridge, M., 2012, Transdimensional Monte Carlo Inversion of AEM Data: 22th International Geophysical Conference and Exhibition, Brisbane, Australia, 2629.

    Google Scholar 

  • Carrigy, M. A., and Kramers, J. W., 1973, Guide to the Athabasca oil sands area, Alberta Research Council Press, Edmonton.

    Google Scholar 

  • Chen, T. Y., Hodges, G., and Miles, P., 2015, MULTIPULSE-High resolution and high power in one TDEM system: Exploration Geophysics, 46, 49–57.

    Article  Google Scholar 

  • Chunduru, R. K., Sen, M. K., and Stoffa, P. L., 1996, 2-D resistivity inversion using spline parameterization and simulated annealing: Geophysics, 62(1), 151–161.

    Article  Google Scholar 

  • Green, P. J., 1995, Reversible jump Markov chain Monte Carlo computation and Bayesian model determination: Biometrika, 82(4), 711–732.

    Article  Google Scholar 

  • Guo, R. W., Dosso, S. E., Liu, J. X., et al., 2011, Nonlinearity in Bayesian 1-D magnetotelluric inversion: Geophysical Journal International, 185(2), 663–675.

    Article  Google Scholar 

  • Hawkins, R., Brodie, R. C., and Sambridge, M., 2017, Trans-dimensional Bayesian inversion of airborne electromagnetic data for 2D conductivity profiles: Exploration Geophysics, 49(3), 332–343.

    Google Scholar 

  • Liu, W. J., Liu, J. X., Guo, R. W., et al., 2011, Correlated data errors on 1-D magnetotelluric (MT) Bayesian inversion: 27th Chinese Geophysical Society, 242.

    Google Scholar 

  • Liu, Y. H., and Yin, C. C., 2016, 3D inversion for multipulse airborne transient electromagnetic data: Geophysics, 81(6), E401–E408.

    Article  Google Scholar 

  • Malinverno, A., 2002, A Bayesian criterion for simplicity in inverse problem parametrization: Geophysical Journal International, 140(2), 267–285.

    Article  Google Scholar 

  • Malinverno, A., 2002, Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem: Geophysical Journal International, 151(3), 675–688.

    Article  Google Scholar 

  • Minsley, B. J., 2011, A trans-dimensional Bayesian Markov chain Monte Carlo algorithm for model assessment using frequency-domain electromagnetic data: Geophysical Journal International, 187(1), 252–272.

    Article  Google Scholar 

  • Mitsuhata, Y., Uchida, T., and Amano, H., 2002, 2.5-D inversion of frequency-domain electromagnetic data generated by a grounded-wire source: Geophysics, 67(6), 1753–1768.

    Article  Google Scholar 

  • Nabighian, N. M., 1988, Electromagnetic Methods in Applied Geophysics-Theory: Society of Exploration Geophysicists Press.

    Book  Google Scholar 

  • Ray, A., and Key, K., 2012, Bayesian inversion of marine CSEM data with a trans-dimensional self parametrizing algorithm: Geophysical Journal International, 191(3), 1135–1151.

    Google Scholar 

  • Ray, A., Key, K., Bodin, T., et al., 2014, Bayesian inversion of Marine CSEM data from the Scarborough gas field using a trans-dimensional 2-D parametrization: Geophysical Journal International, 199(3), 1847–1860.

    Article  Google Scholar 

  • Shi, X. D., Liu, J. X., Guo, R. W., et al., 2012, Unbiased 1D magnetotelluric Bayesian inversion: Computing Techniques for Geophysical and Geochemical Exploration, 34(4), 371–379.

    Google Scholar 

  • Shi, X. M., Wang, J. Y., Zhang. S. Y., et al., 2000, Multiscale genetic algorithm and its application in magnetotelluric sounding data inversion: Chinese Journal Geophysics (in Chinese), 43(1), 122–130.

    Google Scholar 

  • Steuer, A., Siemon, B., and Auken, E., 2009, A comparison of helicopter-borne electromagnetics in frequencyand time-domain at the Cuxhaven valley in Northern Germany: Journal of Applied Geophysics, 67(3), 194–205.

    Article  Google Scholar 

  • Stolz, E. M., and Macnae, J., 1998, Evaluating EMwaveforms by singular value decomposition of exponential basis functions: Geophysics, 63(1), 64–74.

    Article  Google Scholar 

  • Traninorguitton, W., and Hoversten, G. M., 2011, Stochastic inversion for electromagnetic geophysics: Practical challenges and improving convergence efficiency: Geophysics, 76(6), F373–F386.

    Article  Google Scholar 

  • Wang, H., Jiang, H., Wang, L., et al., 2015, Magnetotelluric inversion using artificial neural network: Journal of Central South University (Science and Technology), 46(5), 1707–1714.

    Google Scholar 

  • Wang, Y. R., Wang, L. Q., Yin, D. W., et al., 2014, Airborne electromagnetic data de-convolution based on singular value decomposition of the tau domain: Measurement and Control Technology, 33(1), 47–50.

    Google Scholar 

  • Yin, C. C., and Hodges, G., 2007, Simulated annealing for airborne EMinversion: Geophysics, 72(4), F189–F196.

    Article  Google Scholar 

  • Yin, C. C., Huang, Q., and Ben, F., 2013, The full-time electromagnetic modeling for time-domain airborne electromagnetic systems: Chinese Journal Geophysics (in Chinese), 56(09), 3153–3162.

    Google Scholar 

  • Yin, C. C., Qin, Y. F., Liu, Y. H., and Cai, J., 2014, Transdimensional Bayesian inversion of frequency-domain airborne EMdata: Chinese Journal Geophysics (in Chinese), 57(09), 2971–2980.

    Google Scholar 

  • Yin, C. C., Qiu, C. K., Liu, Y. H., et al., 2016, Weighted laterally-constrained inversion of time-domain airborne electromagnetic data: Journal of Jilin university (Earth Science Edition), 46(1), 254–261.

    Google Scholar 

  • Zhu, K. G., Ma, M. Y., Che, H. W., et al., 2012, PC-based artificial neural network inversion for airborne timedomain electromagnetic data: Applied Geophysics, 9(1), 1–8.

    Article  Google Scholar 

Download references

Acknowledgments

We are grateful to the reviewers and AP editors for comments and suggestions that improved the manuscript. We also wish to thank the members of the electromagnetic research group at Jilin University.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Bo Zhang.

Additional information

This paper was financially supported by the Key National Research Project of China (Nos. 2017YFC0601900 and 2016YFC0303100), and the Key Program of National Natural Science Foundation of China (No. 41530320) and Surface Project (No. 41774125).

Gao Zong-Hui, a master graduate student. In 2017, she obtained a bachelor’s degree from the College of Geo-Exploration Science and Technology, Jilin University. Now she is studying for a master’s degree at the College of Geo-Exploration Science and Technology, Jilin University. Her major is earth exploration and information technology. She mainly engages in research on geophysical electromagnetic forward and inverse theory as well as method techniques.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Gao, ZH., Yin, CC., Qi, YF. et al. Transdimensional Bayesian inversion of time-domain airborne EM data. Appl. Geophys. 15, 318–331 (2018). https://doi.org/10.1007/s11770-018-0684-7

Download citation

  • Received:

  • Revised:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11770-018-0684-7

Keywords

Navigation