AI-Powered Mineral Estimation and Site Selection

Industry: Mineral Exploration and Mining Geology

Context

Traditional mineral exploration is a high-risk, high-cost endeavor with low success rates. This case study outlines Zwilling Labs’ AI-driven approach to integrate geophysical data and geological modeling to optimize borehole drilling.

The Problem

Geological datasets (magnetic surveys, topography, borehole logs) are often siloed. Interpreting 3D magnetic inversions manually is time-consuming and prone to subjective bias, leading to dry boreholes and wasted exploration budgets.

Technical Methodology

Our solution leverages a specialized AI platform designed for multimodal geological data.

A. Data Ingestion & Tabularization:

The platform ingests diverse datasets including:

· Topographic: DSM (Digital Surface Model) and DTM (Digital Terrain Model) derived from drone surveys.

· Geophysical: Total Magnetic Intensity (TMI) maps and transformed derivatives.

· Assays: Borehole logs, trench data, and pit samples.

B. AI-Based Borehole Optimization:

Using machine learning algorithms, the system analyzes 3D magnetic inversion models to identify high probability ‘Target Zones’. The AI recommends specific GPS coordinates and depths for new boreholes, maximizing the likelihood of intersecting ore bodies.

AI-powered mineral exploration workflow showcasing data fusion from magnetic, topographic, and borehole data to identify high-probability targets with precise GPS coordinates, ready for integration with geological software.

Strategic Impact

· Risk Mitigation: Data-driven selection of drilling sites reduces the number of non-productive boreholes.

· Estimation Accuracy: Improved confidence intervals for mineral resource estimation (Measured, Indicated, and Inferred).