Accurate soil stratification is essential for geotechnical engineering design. Owing to its effectiveness and efficiency, the cone penetration test (CPT) has been widely applied for subsurface stratigraphy, which relies heavily on empiricism for correlations to soil type. Recently, deep learning techniques have shown great promise in learning the relationship between CPT data and soil boundaries automatically. However, the segmentation of soil boundaries is fraught with model and measurement uncertainty. This paper introduces an uncertainty‐guided U((‐Net (UGU‐Net) for improved soil boundary segmentation. The UGU‐Net consists of three parts: (a) a Bayesian U‐Net to predict a pixel‐level uncertainty map, (b) reinforcement of original labels on the basis of the predicted uncertainty map, and (c) a traditional deterministic U‐Net, which is applied to the reinforced labels for final soil boundary segmentation. The results show that the proposed UGU‐Net outperforms the existing methods in terms of both high accuracy and low uncertainty. A sensitivity study is also conducted to explore the influence of key model parameters on model performance. The proposed method is validated by comparing the predicted subsurface profile with benchmark profiles. The code for this project is available at github.com/Xiaoqi‐Zhou‐suda/UGU‐Net.

中文翻译：

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使用 Monte Carlo dropout 的不确定性导向 U-Net 用于土壤边界分割


准确的土壤分层对于岩土工程设计至关重要。由于其有效性和效率，圆锥触探试验 （CPT） 已广泛用于地下地层学，该地层学在很大程度上依赖于经验主义与土壤类型的相关性。最近，深度学习技术在自动学习 CPT 数据与土壤边界之间的关系方面显示出巨大的前景。然而，土壤边界的分割充满了模型和测量的不确定性。本文介绍了一种不确定性导向的 U（（‐Net （UGU‐Net） 用于改进土壤边界分割。UGU-Net 由三个部分组成：（a） 用于预测像素级不确定性图的贝叶斯 U-Net，（b） 在预测的不确定性图的基础上对原始标签进行加固，以及 （c） 传统的确定性 U-Net，应用于增强的标签以进行最终的土壤边界分割。结果表明，所提出的 UGU-Net 在高精度和低不确定性方面都优于现有方法。还进行了一项敏感性研究，以探索关键模型参数对模型性能的影响。通过将预测的地下剖面与基准剖面进行比较来验证所提出的方法。该项目的代码可在 github.com/Xiaoqi‐周‐suda/UGU-Net 上找到。