# Machine Learning-Based 3D Soil Layer Reconstruction in Foundation Pit Engineering
## 1 Introduction
The literature proposes a multi-parameter GSIS system for geological modeling which was verified by taking the engineering geological model of Beijing's Shunyi District and the geological model of the central urban area as an example [21]. [PERSON] et al. utilize a machine learning method, specifically the BicycleGAN framework, to address issues in geological reservoir modeling [22]. This method effectively considers local detail features and reduces the impact of conditioning data distribution patterns. [PERSON] et al. employ a deep-learning framework (GANSim-surrogate) to tackle the challenge of integrating geological patterns and various types of data in stochastic conditional geomodeling [23]. [PERSON] et al. propose a method using single-image GAN (SinGAN) to generate nonstationary realizations for subsurface modeling from a single training image [24]. This method effectively evaluates and reconstructs complex geological patterns. [PERSON] et al. propose a hybrid framework method combining multi-point statistics (MPS) and fully connected neural networks (FCNs) for 3D geological modeling, which achieves the goal of retaining the geometry and spatial relationships of strata and faults with a precision of 75% [25]. When processing formation voxels, the voxel data model often requires substantial storage capacity. To address this, a stack-based method is proposed to represent geological surfaces and underground structures. This approach effectively resolves data storage challenges associated with large-scale voxel datasets in the model, thereby solving the voxel data problems with large amounts of data stored by the model [26]. Modeling is challenging in some complex geologies, and a corresponding semi-automatic method for 3D modeling and visualization of complex geological bodies has been proposed [27]. In general, traditional interpolation algorithm modeling is relatively mature, and it can realize the reconstruction of the soil layer on a simple and continuous soil layer. Some improved algorithms based on the basic interpolation method have also played a role to a certain extent. Due to the limitations of the interpolation algorithm, traditional modeling methods perform poorly on complex terrains (such as hiatuses and faults). The interpolation algorithm is affected by the number and location of selected boreholes, and the modeling uncertainty is too large. The scope of the application is relatively limited. On the other hand, machine learning methods have shown promise in addressing some of these challenges [28]. However, there are relatively few studies on the use of machine learning to reconstruct soil layers. There are still several key challenges in applying machine learning to soil layer reconstruction. (1) Acquiring sufficient training data is difficult. Machine learning models require large amounts of labeled data for training. However, current geological data are scattered and not systematically organized, which complicates the process of obtaining the necessary data. (2) There is no mature method for transforming sparse borehole data, which have different dimensions and structures, into a uniform and consistent format suitable for machine learning models. (3) There is a lack of highly adaptable and robust learning models that can be effectively used in engineering environments. The purpose of soil layer reconstruction in deep excavation projects differs from large-scale geological reconstruction in geoscience. The primary goal of pre-construction soil layer reconstruction in deep excavations is to mitigate construction risks. Even in small-scale excavation areas where no significant geological mutations have occurred, it is still necessary to obtain as much information as possible about soil distribution.