Boundary-based random forest leveling of multi-map geochemical data: A case study of the Baiyinchagan-Maodeng Area, Inner Mongolia

Key Points

• Boundary-based random forest effectively levels geochemical data, enhancing data accuracy and reliability.
• The approach was assessed using multi-map geochemical data, improving interpretation in complex geological settings.
• Assessment of geochemical data utilized boundary-based random forest techniques to identify and rectify inconsistencies.
• Findings suggest the method may enable more precise geological assessments in regions like the Baiyinchagan-Maodeng area.