A machine learning-based lithologic mapping method

In this study, a gradient boosting decision tree (GBDT)-based lithologic mapping method constituted by field survey and machine learning is introduced. The Duolong mineral district, Tibet, China is currently chosen for model test. During the practical application, geochemical data at a 1:50000 scale...

Full description

Saved in:
Bibliographic Details
Published inDizhi Lixue Xuebao Vol. 27; no. 3; pp. 339 - 349
Main Authors JI Quanwei, WANG Wenlei, LIU Zhibo, ZHU Maoqiang, YUAN Changjiang
Format Journal Article
LanguageChinese
Published Institute of Geomechanics, Chinese Academy of Geological Sciences 01.06.2021
Subjects
data mining
decision tree
geologic unit
geological mapping
information fusion
Online AccessGet full text

Cover

More Information
Summary:In this study, a gradient boosting decision tree (GBDT)-based lithologic mapping method constituted by field survey and machine learning is introduced. The Duolong mineral district, Tibet, China is currently chosen for model test. During the practical application, geochemical data at a 1:50000 scale is analyzed to identify lithologic units, while a geological map at the same scale currently provides lithologic units identified by field survey. Lithologic units within a small area are firstly collected from the geological map. Correspondence between geochemical data and lithologic units within the small area can consequently be marked, by which the GBDT method is applied to reclassify the geochemical data and further predict lithologic units in the Duolong district. Transforming the result to a probability distribution, areas with low probability can be identified, and further investigation will be implemented to update geological knowledge and correspondence between geochemical and lithologic units. Iteration
ISSN:1006-6616
DOI:10.12090/j.issn.1006-6616.2021.27.03.031