The application of full-decay induced polarization offers additional potential to characterize water-bearing structures. The method enables inversion imaging of four parameters: zero-frequency resistivity, intrinsic polarizability, relaxation time, and frequency-dependent coefficient. Due to the inherent volume effect in electrical exploration, the inversion results often fail to accurately depict the scale of water-bearing structures. To address this limitation, we introduce the fuzzy C-means clustering constraint into the objective function of the full-decay induced polarization inversion. We propose a multi-parameter inversion method for full-decay induced polarization based on fuzzy C-means clustering. To address the inconsistent resolution of anomalies by each induced polarization parameter, we apply different constraints to the sensitivity matrix of each parameter, thereby balancing the resolution of anomalies across parameters. The four parameters are normalized to solve the problem of large order of magnitude gap between different parameters. Inversion imaging numerical simulations of typical water-bearing structures are carried out, and the results showed that the proposed tunnel full-decay induced polarization inversion method based on fuzzy C-mean clustering could effectively depict the position and morphology of the water-bearing structures. Additionally, an on-site application was carried out in the Yinchaojiliao Water Diversion Project, effectively identifying the water body in front of the tunnel face and guiding the on-site construction of the project. The tunnel full-decay induced polarization inversion method based on fuzzy C-mean clustering has the ability to locate and depict boundaries of water-bearing structures with high accuracy.

中文翻译：

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基于模糊 C-means 聚类方法的全衰变诱导极化隧道前含水结构表征


全衰变诱导极化的应用为表征含水结构提供了额外的潜力。该方法能够对四个参数进行反演成像：零频电阻率、本征极化率、弛豫时间和频率相关系数。由于电勘探中固有的体积效应，反演结果往往无法准确描述含水结构的规模。为了解决这一限制，我们将模糊 C-means 聚类约束引入全衰减诱导偏振反转的目标函数中。我们提出了一种基于模糊 C-means 聚类的全衰变诱导极化多参数反转方法。为了解决每个诱导极化参数对异常的分辨率不一致的问题，我们对每个参数的灵敏度矩阵应用了不同的约束，从而平衡了不同参数之间异常的分辨率。对 4 个参数进行归一化，以解决不同参数之间存在大数量级差距的问题。对典型的含水结构进行了反演成像数值模拟，结果表明，所提出的基于模糊 C 均值聚类的隧道全衰变诱导极化反演方法可以有效刻画含水结构的位置和形态。此外，在阴朝吉寮引水工程中开展了现场应用，有效识别了掌子面前水体，指导了项目的现场施工。基于模糊 C-mean 聚类的隧道全衰变诱导极化反演方法能够高精度地定位和描绘含水结构的边界。