Title: A Novel Soil Profile Standardization Technique with XGBoost Framework for Accurate Surface Wave Inversion Show Chinese title

Title: 一种基于XGBoost框架的新型土壤剖面标准化技术用于准确的表面波反演

Abstract: The inversion of surface wave dispersion curves poses significant challenges due to the non-uniqueness, nonlinear, & ill-posed nature of the problem. Local search methods get trapped in suboptimal minima, whereas global search methods are computationally intensive. The CPU time becomes challenging when dealing with a large number of traces, such as 2D/3D surface wave surveys or DAS surveys. Several attempts have been made to perform inversion using machine learning to improve accuracy and reduce CPU times. Current machine learning methods rely on a fixed number of soil layers in the training dataset to maintain a consistent output size, limiting these models to predicting only a narrow range of soil profiles. Consequently, no single machine learning model can effectively predict soil profiles with a wide range of shear wave velocities and varying numbers of layers. The present study introduces a novel soil profile standardization technique and proposes a regression-based XGBoost algorithm to efficiently estimate shear wave velocity profiles for stratified media with a varying number of layers. The proposed model is trained using 10 million synthetic soil profiles. This extensive dataset enables our XGBoost model to learn effectively across a wide range of shear wave velocities. Additionally, the study proposes constraints on the differences in shear wave velocities between consecutive layers and on their ratio with layer thickness, preventing the formation of unrealistic layers and ensuring the predictive model reflects real-world conditions. The effectiveness of our proposed algorithm is demonstrated by adopting a wide range of soil profiles from published literature and comparing the results with traditional inversion methods. The model performs well in a wide range of S-wave velocities and can accurately capture any number of layers of the soil profile during the inversion process Show Chinese abstract

Abstract: 反演表面波频散曲线由于问题的非唯一性、非线性和不适定性而面临重大挑战。 局部搜索方法容易陷入次优最小值，而全局搜索方法计算量大。 当处理大量数据时，如二维/三维表面波调查或DAS调查，CPU时间变得具有挑战性。 已经进行了多次尝试，使用机器学习进行反演以提高准确性并减少CPU时间。 当前的机器学习方法依赖于训练数据集中固定数量的土壤层，以保持一致的输出大小，限制了这些模型只能预测狭窄范围的土壤剖面。 因此，没有单一的机器学习模型能够有效预测具有广泛剪切波速度和不同层数的土壤剖面。 本研究引入了一种新的土壤剖面标准化技术，并提出了一种基于回归的XGBoost算法，以高效估算具有不同层数的分层介质的剪切波速度剖面。 所提出的模型使用1000万个人工合成土壤剖面进行训练。 这个大规模的数据集使我们的XGBoost模型能够在广泛的剪切波速度范围内有效学习。 此外，该研究提出了对相邻层之间剪切波速度差异及其与层厚度比值的约束，防止形成不现实的层，并确保预测模型反映现实条件。 通过采用发表文献中的广泛土壤剖面并将其结果与传统反演方法进行比较，证明了我们所提出算法的有效性。 该模型在广泛的S波速度范围内表现良好，并且在反演过程中可以准确捕捉土壤剖面的任意层数。