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Electrode array and data density effects in 3D induced polarization tomography and applications for mineral exploration

  • Gang ZhangEmail author
  • Qing-Tian LüEmail author
  • Pin-Rong Lin
  • Gui-Bin Zhang
Original Paper
  • 16 Downloads

Abstract

Synthetic resistivity and chargeability models are employed to study the electrode array and data density effects in inversion resolution for 3D induced polarization tomography. Results from the inversion tests with different electrode arrays illustrate that the inversion resolution for electrical resistivity tomography (ERT) and induced polarization tomography (IPT) data with the dipole–dipole array perform better than the pole–pole array and pole–dipole array. In addition, the anomalous bodies with high impedance and high chargeability are hard to be reconstructed. In contrast, the ones with low resistivity and high chargeability can be well established. Results from the inversion tests with different data densities illustrate that the higher data density will yield better inversion resolution for the ERT and IPT data. We firstly employ the sensitivity analysis method and depth of investigation (DOI) index method to evaluate the inversion results. Then, we present the observation-to-parameter ratio (OPR) and model-drift ratio (MDR) for discussing and evaluating the quality of the inverted maps, and the result shows that the correlation can reach to the limit when the data density reaches a certain OPR value of 44. Results from the synthetic inversion tests generally provide reference and guidance for employing the measuring electrode with a suitable arrangement and reasonable number in the field ERT and IPT surveys. Lastly, IPT method with dipole–dipole array has been employed in the mining area with the better prospect of mineral exploration northwest of Gansu Province, China. Moreover, the inverted maps reveal three ore bodies with high resolution.

Keywords

Induced polarization tomography Inversion Electrode array Data density Observation-to-parameter ratio Model-drift ratio Mineral exploration 

Notes

Acknowledgements

This work is co-supported by the Major Program of the National Natural Science Foundation of China under grant no. 41630320, the National Key Research and Development Program of China under grant no. 2016YFC0600201, the National Key Foundation for Exploring Scientific Instrument of China under grant no. 2011YQ050060, the National High Technology Research and Development Program of China (863 Program) under grant no. 2014AA06A610, and the Program of China Postdoctoral Science Foundation (no. 2016M601087). GZ is grateful for research support from SinoProbe Center - Deep Exploration in China, Chinese Academy of Geological Sciences, and School of Geophysics and Information Technology, China University of Geosciences (Beijing). The authors thank the reviewers and editors for giving the constructive suggestions.

Supplementary material

12517_2019_4341_MOESM1_ESM.py (3 kb)
ESM 1 (PY 2 kb)

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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.School of Geophysics and Information TechnologyChina University of GeosciencesBeijingChina
  2. 2.Institute of Geophysical and Geochemical ExplorationChinese Academy of Geological SciencesLangfangChina
  3. 3.SinoProbe Center - Deep Exploration in ChinaChinese Academy of Geological SciencesBeijingChina

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