Inversion of multiple data sets acquired by different array configuration of geoelectrical resistivity method

  • Affiliations:

    Faculty of Oil and Gas, Hanoi University of Mining and Geology, Vietnam

  • *Corresponding:
    kieuduythong@humg. edu. vn
  • Received: 15th-Dec-2019
  • Revised: 6th-Jan-2020
  • Accepted: 28th-Feb-2020
  • Online: 28th-Feb-2020
Pages: 52 - 60
Views: 2514
Downloads: 1612
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The geoelectrical resistivity method is one of the most commonly used geophysical methods. This method uses different electrodes configuration, electrode array, depending on the purpose and conditions of the field, each type of array has its advantages and disadvantages. Due to the development of data acquisition technology, it is common for geoelectrical instruments enable to record data arising from different electrode arrays with negligible real-time construction. However, current software’s only allows to process for each individual electrode array. Inverted models of different electrode array can be integrated to build a common earth model. However, due to the nature of the geophysical inversion is non-unique solutions, it means that there will be an infinite of models that can be suitable for a measurement in a certain noise level. This leads to the same measurement data in an area with different electrode array may produce different geoelectrical models making the dificulty for integration process. To solve this problem, we utilise the simultaneous joint inversion algorithm of data sets arising from multiple electrode arrays. The test results on synthetic data show that this combination is better than the solution of each individual electrode array. The best result is a combination of pole - dipole (PD), dipole - pole (DP) and dipole - dipole (DD).

How to Cite
Kieu, T.Duy 2020. Inversion of multiple data sets acquired by different array configuration of geoelectrical resistivity method (in Vietnamese). Journal of Mining and Earth Sciences. 61, 1 (Feb, 2020), 52-60. DOI:

Akca, I. (2016). ELRIS2D: A MATLAB package for the 2D inversion of DC resistivity/IP data. Acta Geophysica, Vol. 64, No. 2, pp. 443-462.

Athanasiou, E.N., Tsourlos, P.I., Papazachos, C.B., and Tsokas, G.N. (2007). Combined weighted inversion of electrical resistivity data arising from different array types. Journal of Applied Geophysics, Vol. 62, No. 2, pp. 124-140.

Constable, S.C., Parker, R.L., and Constable, C.G. (1987). Occam’s inversion: A practical algorithm for generating smooth models from electromagnetic sounding data. Geophysics, Vol. 52, No. 3, pp. 289-300.

Constable, S., Orange, A., and Key, K. (2015). And the geophysicist replied: “Which model do you want?”: Geophysics, Vol. 80, No. 3, pp. E197-E212.

Dahlin, T., Zhou, B. (2004). A numerical comparison of 2D resistivity imaging with 10 electrode arrays. Geophysical Prospecting, Vol. 52, No. 5, pp. 379-398. -(2006). Multiple-gradient array measurements for multichannel 2D resistivity imaging. Near Surface Geophysics, Vol. 4, No. 2, pp. 113-123.

Gallardo, L. A., and Meju, M. A. (2011). Structure-coupled multiphysics imaging in geophysical sciences. Reviews of Geophysics, Vol. 49, No. 1.

Heincke, B., Jegen, M., Moorkamp, M., Hobbs, R.W., and Chen, J. (2017). An adaptive coupling strategy for joint inversions that use petrophysical information as constraints. Journal of Applied Geophysics, Vol. 136, pp. 279-297.

Lines, L., Schultz, A., and Treitel, S. (1988). Cooperative inversion of geophysical data. Geophysics, Vol. 53, No. 1, pp. 8-20.

Menke, W. (2015). Review of the Generalized Least Squares Method. Surveys in Geophysics, Vol. 36, No. 1, pp. 1-25.

Moorkamp, M. (2017). Integrating electromagnetic data with other geophysical observations for enhanced imaging of the earth: a tutorial and review. Surveys in Geophysics, Vol. 38, No. 5, pp. 935-962.

Moorkamp, M., Heincke, B., Jegen, M., Roberts, A.W., and Hobbs, R.W. (2011). A framework for 3-D joint inversion of MT, gravity and seismic refraction data. Geophysical Journal International, Vol. 184, No. 1, pp. 477-493.

Paasche, H., and Tronicke, J. (2007). Cooperative inversion of 2D geophysical data sets: a zonal approach based on fuzzy c-means cluster analysis. Geophysics, Vol. 72, No. 3, pp. 35-39.

Sun, J., and Li, Y. (2014). Adaptive Lp inversion for simultaneous recovery of both blocky and smooth features in a geophysical model. Geophysical Journal International, Vol. 197, No. 2, pp. 882-899; (2015), Multidomain petrophysically constrained inversion and geology differentiation using guided fuzzy c-means clustering. Geophysics, Vol. 80, No. 4, pp. ID1-ID18.

Szalai, S., and Szarka, L. (2008). On the classification of surface geoelectric arrays. Geophysical Prospecting, Vol. 56, No. 2, pp. 159-175.

Szalai, S., Novák, A., and Szarka, L. (2011). Which geoelectric array sees the deepest in a noisy environment? Depth of detectability values of multielectrode systems for various two-dimensional models. Physics and Chemistry of the Earth, Parts A/B/C 36(16), pp. 1398-1404.

Tarantola, A., and Valette, B. (1982). Generalized nonlinear inverse problems solved using the least squares criterion: Reviews of Geophysics, Vol. 20, No. 2, pp. 19-232.

Tikhonov, A.N., Arsenin, V.J., Arsenin, V.I., and Arsenin, V.Y. (1977). Solutions of ill-posed problems. Vh Winston.

Thong, K.D. (2018). Overview of the decomposition of geophysical documents. In the conference of earth sciences and natural resources for sustainable development (ERSD 2018), Hanoi University of Mining and Geology, pp. 70-76. (in Vietnamese)

Vozoff, K., and Jupp, D. L. B., (1975a). Joint Inversion of Geophysical Data: Geophysical Journal of the Royal Astronomical Society, Vol. 42, No. 3, pp. 977-991.-, (1975b), Joint Inversion of Geophysical Datam, Vol. 42, No. 3, pp. 977-991.