Abstract: 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.

Key words: machine learning;support vector machine, 3D geological modeling;implicit modeling, borehole data