基于机器学习的钻孔数据隐式三维地质建模方法

针对基于钻孔数据的传统显式三维地质建模方法存在过程繁琐、模型质量难以保证等缺点，本文提出了一种基于机器学习的隐式三维地质建模方法，将地层三维建模问题转换为地下空间栅格单元的属性分类问题.分别基于支持向量机、BP神经网络等分类算法，实现了钻孔数据的自动三维地质建模.实际建模结果表明，对于有限、稀疏的钻孔数据，支持向量机方法建模准确率较高，建模效率、效果优于显式建模方法.最后通过敏感性分析研究了超参数对建模结果准确率、模型形态的影响，为可控的自动三维地质建模提供了一种新的解决思路.

Implicit 3D Geological Modeling Method for Borehole Data Based on Machine Learning

Considering the complex modeling process and difficulty in guaranteeing the model quality of traditional explicit 3D modeling methods， an implicit 3D geological modeling method for borehole data based on machine learning was proposed， which transformed the strata 3D modeling problem into a process of geological attribute classification of the underground spatial grid units. Based on the classification algorithms of support vector machine and BP neural network， automatic 3D geological modeling from borehole data was realized. The results demonstrate that for sparse and limited borehole data， support vector machine can generally perform better than explicit methods. Finally， the influence of hyper-parameter on modeling accuracy and model shape is studied through sensitivity analysis， which provides a new solution for controllable 3D geological modeling.