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Statistical Estimation of the Depth of Investigation of ERT and IP Surveys Based on a Modified DOI Index Method

机译:基于改进的DOI指数法的ert和IP调查深度估算估计

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Several techniques are available to estimate the depth of investigation (DOI) or to identify possible artifacts in resistivity and IP surveys. Commonly, the DOI is mainly estimated using an arbitrarily chosen cut-off value on a selected resolution indicator (resolution, sensitivity or DOI index). Small changes in threshold values may induce strong variations in the estimated DOI. To overcome this problem, we developed a new statistical method to estimate the DOI based on a modified DOI index approach. Three inversions are performed using three strongly different resistivity reference models. We found that the cumulative distribution function of the DOI index values is well fitted by the sum of two normal distributions. We then focused on the evaluation of the mean and standard deviation of the normal distribution linked to the statistically well-constrained cells. We introduced two reliability indexes RI2σ and RI3σ based on confidence intervals, respectively 2σ and 3σ. They are used as alpha transparency values when plotting resistivity and chargeability models to discriminate between well- and poorly- constrained cells. The efficiency of the proposed methodology is assessed on synthetic data. Based on synthetic benchmark analysis, we demonstrated that the selected well-constrained cells are well- reconstructed in size, shape and resistivity.
机译:有几种技术可用于估计调查深度(DOI)或识别电阻率和IP调查中可能的伪影。通常,DOI主要使用所选分辨率指示符(分辨率,灵敏度或DOI指数)上的任意选择的截止值来估计。阈值的小变化可能会引起估计的DOI的强大变化。为了克服这个问题,我们开发了一种基于修改的DOI索引方法来估计DOI的新统计方法。使用三种强电阻率参考模型进行三个逆。我们发现,DOI指数值的累积分布函数通过两个正常分布的总和齐全。然后,我们专注于评估与统计上良好约束的细胞相关的正态分布的平均值和标准偏差。我们基于置信区间引入了两个可靠性索引RI2σ和RI3σ,分别为2σ和3σ。当绘制电阻率和充电能力模型时,它们用作alpha透明度值,以区分细胞良好和差的细胞。在合成数据上评估所提出的方法的效率。基于合成基准分析,我们证明了所选择的良好约束的细胞尺寸,形状和电阻率均匀重建。

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