Implicit 3D Geological Modeling Based on Machine Learning:A Case Study of Lazigou Gold Deposit in Muping-Rushan Metallogenic Belt
Wang Tongrong
Ji Xubo
Wang Jiangbo
Liu Yang
Shao Yubao
Wang Yongjun
Huang Xin
Gao Tao
Jiang Peng
Shan Jiangtao
Tan Jun
Zhao Zhixin
Abstract：This study aims to develop a methodology for implicit 3D geological modeling under the constraint of limited exploration data availability.Taking Lazigougold deposit as an example,we generate virtual grooving by distance weighting method based on the original grooving sampling data,and use the original grooving data to train and evaluate three machine learning models,namely K-nearest neighbor,random forest and gradient elevator,and select the random forest model with the best prediction performance to discriminate and predict the lithology of virtual grooving.Virtual encryption of groove data is realized by machine learning method,which provides a large number of sample data for implicit 3D modeling.On this basis,the orebody model and Au element grade numerical model of Lazigou gold mine were constructed in implicit modeling software using the original groove and virtual groove data.Five prospecting targets have been delimited,which have been proved reliable by engineering.The implicit 3D modeling based on machine learning can make full use of the known data to predict the unknown region and provide sufficient samples for the implicit 3D modeling,which is conducive to the construction of a higher precision geological model under the existing exploration engineering conditions,and then provide a basis for the deep edge prospecting prediction.
Keywords：Lazigou gold depositimplicit 3D modelingmachine learningrandom forest3D mineral prediction
Publication Date：2025-08-30
Online Publishing Date：2025-08-22(First online date of this platform, not the publication date of the document)
Pages：15( 3167-3181 )