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<p>Soil resistivity has wide-ranging applications in rock geology, and its study is important for characterizing soil properties, hydrological processes, and geological risk assessment. However, many factors influence soil resistivity, which complicates the quantification of relationships between resistivity and these factors. This study conducted resistivity tests on unsaturated loess using the four-electrode method. We systematically analyzed the effects of temperature, water content, and dry density on loess resistivity. A predictive model was developed and optimized using genetic algorithm. Finally, we evaluated the model’s applicability to different soil textures. The results indicate that soil resistivity decreases exponentially with increasing temperature and water content, whereas it decreases linearly with increasing dry density. Among the factors affecting soil resistivity, water content is the dominant factor, followed by temperature, with the influence of dry density being comparatively small. The model proposed in this study yields robust predictive performance and demonstrates strong applicability to other soil types. These findings provide a theoretical basis and reference for monitoring soil resistivity and for parameter-inversion applications.</p>

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Analysis of influencing factors and prediction model of resistivity in unsaturated loess

  • 期刊论文

  • 发表日期：

    2026年5月7日

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  • Ye Wang,
  • Huanhuan Li,
  • Yaning Li,
  • Aoyu Zheng,
  • Yongquan Li,
  • Wenyu Dang

摘要

Soil resistivity has wide-ranging applications in rock geology, and its study is important for characterizing soil properties, hydrological processes, and geological risk assessment. However, many factors influence soil resistivity, which complicates the quantification of relationships between resistivity and these factors. This study conducted resistivity tests on unsaturated loess using the four-electrode method. We systematically analyzed the effects of temperature, water content, and dry density on loess resistivity. A predictive model was developed and optimized using genetic algorithm. Finally, we evaluated the model’s applicability to different soil textures. The results indicate that soil resistivity decreases exponentially with increasing temperature and water content, whereas it decreases linearly with increasing dry density. Among the factors affecting soil resistivity, water content is the dominant factor, followed by temperature, with the influence of dry density being comparatively small. The model proposed in this study yields robust predictive performance and demonstrates strong applicability to other soil types. These findings provide a theoretical basis and reference for monitoring soil resistivity and for parameter-inversion applications.