Due to the complex spatial features of geological formation, it remains a significant challenge to accurately predict geological cross-sections from limited borehole data. This study develops an innovative multi-spatial receptive field (MSRF) XGBoost approach, which encompasses classification and identification modules to forecast geological cross-sections using sparse borehole data. The classification module exclusively employs sparse borehole data to train a series of MSRF XGBoost models for soil classification. The identification module leverages all the trained models to generate potential predictions of unknown soil strata, automatically pinpointing the optimal one via Gaussian filtering and boundary similarity algorithms. A new boundary accuracy criterion is proposed to assess the prediction capacity of different models. Following this, the developed MSRF XGBoost method is compared with an existing conventional XGBoost method using both linear and nonlinear cases. The findings illustrate that our proposed method enhances the prediction accuracy for both linear and nonlinear geological cross-sections. Furthermore, the developed method is employed to determine a geological cross-section in the Netherlands using open-source borehole data. The accuracy of the method in predicting soil layers in all in situ boreholes reaches an impressive 90%, validating its effectiveness in practical geotechnical engineering.

中文翻译：

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基于稀疏钻孔数据预测地质剖面的新型多空间感受野（MSRF）XGBoost方法


由于地质构造的复杂空间特征，从有限的钻孔数据准确预测地质剖面仍然是一个重大挑战。本研究开发了一种创新的多空间感受野 (MSRF) XGBoost 方法，其中包含分类和识别模块，可使用稀疏钻孔数据预测地质横截面。分类模块专门使用稀疏钻孔数据来训练一系列用于土壤分类的 MSRF XGBoost 模型。识别模块利用所有经过训练的模型来生成未知土壤层的潜在预测，并通过高斯滤波和边界相似性算法自动确定最佳模型。提出了一种新的边界精度标准来评估不同模型的预测能力。接下来，使用线性和非线性情况将开发的 MSRF XGBoost 方法与现有的传统 XGBoost 方法进行比较。研究结果表明，我们提出的方法提高了线性和非线性地质剖面的预测精度。此外，所开发的方法还用于使用开源钻孔数据确定荷兰的地质剖面。该方法对所有原位钻孔的土层预测准确率达到了令人印象深刻的90%，验证了其在实际岩土工程中的有效性。