Previous Article in Journal
Geochemical Profile Characterization of Mine Tailings by Exploited Element as Input for Receptor Models: Case of Chilean Tailings (Cu-Au-Ag-Mo-Fe-Zn-Pb-Kaolin-CaCO3)
Previous Article in Special Issue
The Application of Sulfur–Metal Mass Ratios in Metal Sulfides in Assessing Prospects for Deep Metallogeny: A Case Study of the Tongshan Copper Deposit in Heilongjiang Province, Northeast China
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Minerals
Volume 16
Issue 1
10.3390/min16010006
minerals-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China

by
Lichun Fu
1,2,
Guihu Chen
1,2,
Qingyuan Song
1,2,
Tiankun Xie
1,2,
He Yuan
1,2,
Xuefeng Li
3,
Yu Su
3,
Keyan Xiao
4,5 and
Rui Tang
4,5,*
1
Harbin Center for Integrated Natural Resources Survey, China Geological Survey, Harbin 150086, China
2
Observation and Research Station of Earth Critical Zone in Black Soil, Ministry of Natural Resources, Harbin 150086, China
3
Heilongjiang Institute of Natural Resources Survey, Harbin 150036, China
4
Geomathematics Key Laboratory of Sichuan Province, Chengdu University of Technology, Chengdu 610059, China
5
SinoProbe Laboratory, Institute of Mineral Resources, Chinese Academy of Geological Sciences, Beijing 100037, China
*
Author to whom correspondence should be addressed.
Minerals 2026, 16(1), 6; https://doi.org/10.3390/min16010006 (registering DOI)
Submission received: 19 November 2025 / Revised: 10 December 2025 / Accepted: 18 December 2025 / Published: 20 December 2025
(This article belongs to the Special Issue Geochemical Exploration for Critical Mineral Resources, 2nd Edition)
Versions Notes

Abstract

The close spatial and genetic coexistence of Skarn-type and Hydrothermal Vein-type Pb-Zn deposits in the Shanghulin area, Inner Mongolia, poses a significant challenge to conventional “ undifferentiated” prediction models. This study aims to decouple these distinct metallogenic patterns using a data-driven, “type-specific modeling” strategy, establishing separate prediction models for Skarn-type and Hydrothermal Vein-type mineralization. Our workflow first employs Lasso–RFECV for rigorous pre-screening of over 60 geoscience features to identify the optimal predictive subset. Subsequently, an XGBoost model is trained on these selected features, and the SHAP framework is applied to interpret the geological significance of its decision logic. The results confirm two distinct indicator systems. (1) The Skarn-type model is controlled by spatial proximity to a heat source, heavily relying on Distance_to_Volcano and high-temperature indicators (CLR_Mo, CLR_W, CLR_Mn). (2) The Hydrothermal Vein-type model is “chemical fingerprint-driven”, prioritizing CLR_Y and identifying a complex “leaching-enrichment” pattern: mineralization requires simultaneous wall-rock leaching (low CLR_Al2O3, low CLR_Y) and specific metal enrichment (high CLR_Co, high CLR_Zn). This study confirms the controlling factors: Skarn-type deposits are governed by magmatic proximity, whereas Hydrothermal Vein-type deposits are defined by specific alteration geochemical signatures. The proposed “Lasso–RFECV → XGBoost → SHAP” workflow successfully decouples these independent, geologically meaningful prospectivity models from complex data, offering a new paradigm for precise exploration.
Keywords: XGBoost; explainable machine learning; mineral prospectivity mapping; Pb-Zn deposit XGBoost; explainable machine learning; mineral prospectivity mapping; Pb-Zn deposit

Share and Cite

MDPI and ACS Style

Fu, L.; Chen, G.; Song, Q.; Xie, T.; Yuan, H.; Li, X.; Su, Y.; Xiao, K.; Tang, R. Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China. Minerals 2026, 16, 6. https://doi.org/10.3390/min16010006

AMA Style

Fu L, Chen G, Song Q, Xie T, Yuan H, Li X, Su Y, Xiao K, Tang R. Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China. Minerals. 2026; 16(1):6. https://doi.org/10.3390/min16010006

Chicago/Turabian Style

Fu, Lichun, Guihu Chen, Qingyuan Song, Tiankun Xie, He Yuan, Xuefeng Li, Yu Su, Keyan Xiao, and Rui Tang. 2026. "Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China" Minerals 16, no. 1: 6. https://doi.org/10.3390/min16010006

APA Style

Fu, L., Chen, G., Song, Q., Xie, T., Yuan, H., Li, X., Su, Y., Xiao, K., & Tang, R. (2026). Data-Driven Decoupling of Metallogenic Patterns: A Case Study of Skarn-Type vs. Hydrothermal Vein-Type Pb-Zn Deposits in the Shanghulin Area, Inner Mongolia, China. Minerals, 16(1), 6. https://doi.org/10.3390/min16010006

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Article metric data becomes available approximately 24 hours after publication online.
Back to TopTop