Subsurface stratification is crucial for the construction safety of underground projects. The one-dimensional (1D) soil stratification aims at identifying segmentation points that separate soil strata. Current engineering practice mainly requires human judgement, which is time-consuming, labour-intensive, and heavily relies on domain expertise. Other probabilistic methods, such as Bayesian approaches, usually involve complex expressions. With the advent of artificial intelligence, deep learning has emerged as a powerful tool in various domains. The UNet, as a typical convolutional neural network, has been extensively utilized for its superior performance in segmentation tasks, but struggles to capture global and long-range semantic information due to the locality of convolution operations. To realize intelligent and automatic 1D soil stratification, this paper introduces a UNet-like Transformer (ULTra) that integrates multiple data sources, including cone penetration test and borehole data, to incorporate prior knowledge. The architecture features a multi-level Transformer with shifted windows in both the encoder and decoder to extract context features and restore spatial resolution, respectively. Experimental results demonstrate that the ULTra outperforms other UNet variants, particularly in detecting minor textures and local details, underscoring the benefits of integrating Transformers into a standard UNet. Case studies indicate that compared with probabilistic methods, the ULTra enables automatic 1D soil stratification using original exploration data with less human intervention, which is fast, effective, and could be continuously improved through interaction with human knowledge, thus streamlining the intelligent data analysis.

中文翻译：

---

使用锥体触探测试和钻孔数据进行一维土壤分层的 UNet 类变压器


地下分层对于地下工程的施工安全至关重要。一维 （1D） 土壤分层旨在识别分隔土壤层的分割点。当前的工程实践主要需要人工判断，这很耗时、劳动密集，并且严重依赖领域专业知识。其他概率方法（如贝叶斯方法）通常涉及复杂表达式。随着人工智能的出现，深度学习已成为各个领域的强大工具。UNet 作为一种典型的卷积神经网络，因其在分割任务中的卓越性能而被广泛使用，但由于卷积操作的局部性，难以捕获全局和远程语义信息。为了实现智能和自动化的一维土壤分层，本文引入了一种类似 UNet 的变压器 （ULTra），它集成了多个数据源，包括锥体触探测试和钻孔数据，以整合先验知识。该架构具有一个多级 Transformer，编码器和解码器中都有移动窗口，可分别提取上下文特征和恢复空间分辨率。实验结果表明，ULTra 的性能优于其他 UNet 变体，尤其是在检测次要纹理和局部细节方面，强调了将 Transformer 集成到标准 UNet 中的好处。案例研究表明，与概率方法相比，ULTra 能够使用原始勘探数据进行自动 1D 土壤分层，人工干预更少，快速、有效，并且可以通过与人类知识的交互不断改进，从而简化智能数据分析。