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<p>In transient electromagnetic (TEM) surveys, the presence of chargeable materials in the exploration area can induce polarization effects, which influence the electromagnetic field and distort the TEM data. Traditional inversion methods that only account for resistivity may not provide accurate results under such conditions. This paper addresses this issue by employing a dispersive resistivity model to simulate TEM data that incorporates both electromagnetic induction and induced polarization eff ects. We use a Bayesian inversion framework to extract resistivity and induced polarization parameters (such as chargeability, time constant, and frequency dependency from the Cole-Cole model) from the TEM data. The Bayesian inversion method off ers confi dence intervals for the inversion results, providing a quantitative assessment of the inherently non-unique multi-parameter inversion outcomes. Numerical simulations and inversion examples show that it is possible to accurately and reliably extract both resistivity and induced polarization parameters from TEM data, particularly for low-resistivity and high-polarization models. However, accurately recovering all target parameters remains challenging for resistive, chargeable bodies. Our approach was successfully applied to TEM data from Keyou Qianqi in Inner Mongolia, where we extracted the resistivity and induced polarization parameters of a conductive, high-polarization silver-lead-zinc ore body.</p>

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Inversion of IP-affected TEM data with full parametrization of dispersive resistivity

  • 期刊论文

  • 发表日期：

    2025年6月3日

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  • Hai Li,
  • Ke-ying Li,
  • Zi-teng Li,
  • Wei-li Guo

摘要

In transient electromagnetic (TEM) surveys, the presence of chargeable materials in the exploration area can induce polarization effects, which influence the electromagnetic field and distort the TEM data. Traditional inversion methods that only account for resistivity may not provide accurate results under such conditions. This paper addresses this issue by employing a dispersive resistivity model to simulate TEM data that incorporates both electromagnetic induction and induced polarization eff ects. We use a Bayesian inversion framework to extract resistivity and induced polarization parameters (such as chargeability, time constant, and frequency dependency from the Cole-Cole model) from the TEM data. The Bayesian inversion method off ers confi dence intervals for the inversion results, providing a quantitative assessment of the inherently non-unique multi-parameter inversion outcomes. Numerical simulations and inversion examples show that it is possible to accurately and reliably extract both resistivity and induced polarization parameters from TEM data, particularly for low-resistivity and high-polarization models. However, accurately recovering all target parameters remains challenging for resistive, chargeable bodies. Our approach was successfully applied to TEM data from Keyou Qianqi in Inner Mongolia, where we extracted the resistivity and induced polarization parameters of a conductive, high-polarization silver-lead-zinc ore body.