IDENTIFICATION OF TARGETS IN A SILVER VEIN DISTRICT USING MACHINE LEARNING *J.B. Harris¹, L. Lugones¹, A. Rochefort¹, T. Rodriguez¹, M. Contreras¹, J. Muñóz¹ ¹Mineral Forecast, Santiago, Chile. (*Presenting author: jacqueline.harris@mineralforecast.cl) ABSTRACT Mineral exploration in mature brownfield districts faces the critical challenge of identifying new high-potential targets amidst complex geological settings where traditional methods often reach their limits, leading to increased costs and slower discovery rates. This study addresses this problem by implementing an advanced Machine Learning (ML) framework to integrate and analyze multi-dimensional data from a low-sulfidation epithermal silver district in Mexico. The solution involved a two-stage methodology: first, a technological validation using clustering to verify the model’s predictive capabilities in known sectors (Veta Pérez), and second, the evaluation of 50 different ML architectures to select the optimal K-Neighbors model. This specific architecture was chosen for its superior ability to maximize precision and minimize false positives, which is crucial for high-cost drilling decisions. The model successfully integrated 45 variables, including geophysics, lithology, and structural data from over 230,600 meters of historical drilling, to identify 30 new areas of interest within a 248 km² area. The practical impact of this approach is demonstrated by a 94.74% accuracy and a 70.86% precision rate in the final predictions. Most significantly, out of 34 targets generated by the model and subsequently drilled, 20 yielded positive mineralization (Ag > 50 ppm), achieving a 59% success rate. These results prove that the proposed ML workflow provides a reliable, data-driven guide that significantly optimizes exploration budgets and timelines, directly contributing to the industry's goal of delivering minerals faster and smarter. This methodology directly addresses the global challenge of delivering minerals faster and smarter by transforming historical data into high-confidence predictive model exploration targets. KEYWORDS Machine Learning, Mineral Exploration, Silver Veins, Predictive Modeling, WMC 2026. 1. INTRODUCTION We identify Several preliminary tests were conducted new predictive model exploration targets through the synthesis of multi-dimensional data is a primary driver for integrating Machine Learning (ML) into the mining industry. This project is situated near an active underground operation in Mexico with a history dating back to 1757. The deposit is a low-sulfidation epithermal vein system characterized by rigorous structural control. The primary objective is to identify new areas of interest across 248 km². The challenge lies in integrating 45 disparate variables including lithology, hydrothermal alteration, and
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