Title: Advancing resistivity-chargeability modeling for complex subsurface characterization using machine learning and deep learning Show Chinese title

Title: 使用机器学习和深度学习推进复杂地下表征的电阻率-极化率建模

Abstract: Subsurface lithological heterogeneity presents challenges for traditional geophysical methods, particularly in resolving nonlinear electrical resistivity and induced polarization (IP) relationships. This study introduces a data-driven machine learning and deep learning (ML/DL) framework for predicting 2D IP chargeability models from resistivity, depth, and station distance, reducing reliance on field IP surveys. The framework integrates ensemble regressors with a one-dimensional convolutional neural network (1D CNN) enhanced by global average pooling. Among the tested models, CatBoost achieved the highest prediction accuracy (R^2 = 0.942 training, 0.945 testing), closely followed by random forest, while the stacked ML/DL ensemble further improved performance, particularly for complex resistivity-IP behaviors. Overall accuracy ranged from R^2 = 0.882 to 0.947 with RMSE < 0.04. Integration with k-means clustering enhanced lithological discrimination, effectively delineating sandy silt, silty sand, and weathered granite influenced by saturation, clay content, and fracturing. This scalable approach provides a rapid solution for subsurface modeling in exploration, geotechnical, and environmental applications. Show Chinese abstract

Abstract: 地下岩性异质性对传统地球物理方法构成了挑战，特别是在解析非线性电阻率和激发极化（IP）关系方面。本研究引入了一种数据驱动的机器学习和深度学习（ML/DL）框架，用于从电阻率、深度和测站距离预测二维IP极化模型，减少了对现场IP调查的依赖。该框架集成了集成回归器与一维卷积神经网络（1D CNN），并通过全局平均池化进行增强。在测试的模型中，CatBoost达到了最高的预测精度（训练R^2 = 0.942，测试R^2 = 0.945），紧随其后的是随机森林，而堆叠的ML/DL集成进一步提高了性能，尤其是在复杂电阻率-IP行为方面。总体准确性范围为R^2 = 0.882至0.947，RMSE < 0.04。与k均值聚类的整合增强了岩性识别，有效界定了受饱和度、粘土含量和裂隙影响的砂质粉土、粉砂质砂和风化花岗岩。这种可扩展的方法为勘探、岩土工程和环境应用中的地下建模提供了快速解决方案。