Hydrogeochemical Vectoring to Concealed Porphyry Copper Systems Using Unsupervised Machine Learning: A Case Study from Central British Columbia, Canada
Abstract
Environments with thick cover present a challenge to surficial exploration efforts the Canadian Cordillera has extensive Quaternary glacial till and sediment cover, making it difficult to find hidden ore bodies. An alternative is using hydrogeochemistry as an exploration tool, to detect anomalies associated with mineralization. This study demonstrates the hydrogeochemical vectoring to porphyry copper deposit using unsupervised machine learning and a spatial workflow to explore beneath the cover of British Columbia, Canada. A framework of 3501 stream and groundwater data points was compiled, cleaned for string-censored limits of detection (LOD) and evaluated across 12 physicochemical variables simultaneously. An unsupervised K-Means algorithm successfully separated the groundwater into two clusters: "Fresh Baseline Water" (n=3314) and "Ore-Interacting Anomaly" water (n=187). The univariate evaluation achieved a clean separation of 90.67% dissolved Sulfate, confirming sulfate as an effective exploration vector; the multivariate pipeline resolved the 9.33% overlap zone between background and mineralized samples. A custom trail gradient index (Cu * Mo)/ (Zn) was deployed was deployed to segment the anomalies into three distinct trails: Proximal Hypogene Core (n=75) with mean Mo=16.81ppb, Zn=205.75ppb, a Secondary Reducing Zone (n=21) exhibiting peak copper precipitation; (mean Cu: 1.26ppb) and a Distal Exploration Halo (n=90) with mean Zn=51.04ppb and elevated Electrical Conductivity mean= 580.44μS/cm. QGIS mapping show these clusters align with the major regional fault networks, providing a non-invasive exploration tool.
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BibTeX
@misc{kukudi2026hydrogeochemical,
title = {{Hydrogeochemical Vectoring to Concealed Porphyry Copper Systems Using Unsupervised Machine Learning: A Case Study from Central British Columbia, Canada}},
author = {Boago Felicity Kukudi},
howpublished = {EarthArXiv},
year = {2026},
month = sep,
doi = {10.31223/x5r800},
url = {https://eartharxiv.org/repository/view/15163/},
note = {CC BY 4.0},
}
RIS
TY - UNPB
TI - Hydrogeochemical Vectoring to Concealed Porphyry Copper Systems Using Unsupervised Machine Learning: A Case Study from Central British Columbia, Canada
AU - Boago Felicity Kukudi
PY - 2026
DA - 2026/09/23
PB - EarthArXiv
DO - 10.31223/x5r800
UR - https://eartharxiv.org/repository/view/15163/
AB - Environments with thick cover present a challenge to surficial exploration efforts the Canadian Cordillera has extensive Quaternary glacial till and sediment cover, making it difficult to find hidden ore bodies. An alternative is using hydrogeochemistry as an exploration tool, to detect anomalies associated with mineralization. This study demonstrates the hydrogeochemical vectoring to porphyry copper deposit using unsupervised machine learning and a spatial workflow to explore beneath the cover of British Columbia, Canada. A framework of 3501 stream and groundwater data points was compiled, cleaned for string-censored limits of detection (LOD) and evaluated across 12 physicochemical variables simultaneously. An unsupervised K-Means algorithm successfully separated the groundwater into two clusters: "Fresh Baseline Water" (n=3314) and "Ore-Interacting Anomaly" water (n=187). The univariate evaluation achieved a clean separation of 90.67% dissolved Sulfate, confirming sulfate as an effective exploration vector; the multivariate pipeline resolved the 9.33% overlap zone between background and mineralized samples. A custom trail gradient index (Cu * Mo)/ (Zn) was deployed was deployed to segment the anomalies into three distinct trails: Proximal Hypogene Core (n=75) with mean Mo=16.81ppb, Zn=205.75ppb, a Secondary Reducing Zone (n=21) exhibiting peak copper precipitation; (mean Cu: 1.26ppb) and a Distal Exploration Halo (n=90) with mean Zn=51.04ppb and elevated Electrical Conductivity mean= 580.44μS/cm. QGIS mapping show these clusters align with the major regional fault networks, providing a non-invasive exploration tool.
ER -