AI-Driven Geological Exploration Suite

This suite applies AI to satellite imagery, core scanning, and real-time geosteering to continuously map, characterize, and track subsurface geology at mining sites. By automating interpretation and optimizing drilling and extraction decisions, it increases ore recovery, shortens exploration cycles, and reduces the cost and risk of development programs.

The Problem

Your geology is changing daily while your models update monthly—value is leaking away.

Organizations face these key challenges:

1

Static geological models that lag months behind what’s actually happening on site

2

Highly paid geologists spending days logging cores and images instead of interpreting results

3

Drilling plans based on incomplete or outdated information, leading to missed ore and unnecessary meters

4

Limited visibility into real-time site changes from space, causing compliance, safety, and planning blind spots

5

Geosteering decisions relying on a few experts watching logs 24/7, with inconsistent outcomes

Impact When Solved

Higher ore recovery and fewer dry holesFaster exploration and development cyclesLower technical and capital risk per project

The Shift

Before AI~85% Manual

Human Does

  • Visually inspect and interpret satellite imagery to map pits, waste dumps, roads, and surface changes.
  • Manually log core (lithology, alteration, structures, mineralization) using spreadsheets or logging software.
  • Manually correlate core logs, assays, and structural data into block models and resource estimates.
  • Monitor drilling data in real time and manually steer drill bits to stay in target formations.

Automation

  • Basic GIS processing and visualization of satellite imagery without automated interpretation.
  • Rule‑based or scripted processing of drilling logs and assay data for basic QA/QC.
  • Standard CAD and mine‑planning software for manual model building and pit/stope design.
With AI~75% Automated

Human Does

  • Define geological concepts, economic cut‑offs, and business rules that guide AI models and decision thresholds.
  • Validate and calibrate AI interpretations, focusing on edge cases, new geological domains, and high‑value decisions.
  • Make final calls on drilling programs, pit/stope designs, and development sequencing using AI‑generated models and alerts.

AI Handles

  • Continuously analyze satellite imagery to detect and classify site changes (new pits, dump expansion, road construction) and update surface models.
  • Scan and interpret core images and measurements, automatically classifying lithology, alteration, structures, and mineralization with standardized logs.
  • Fuse core data, assays, and drilling logs into updated 3D geological and resource models on a near‑real‑time basis.
  • Ingest downhole geosteering data and automatically recommend or execute drill path adjustments to stay within target rock units.

Solution Spectrum

Four implementation paths from quick automation wins to enterprise-grade platforms. Choose based on your timeline, budget, and team capacity.

1

Quick Win

Satellite-Guided Target Screening Dashboard

Typical Timeline:Days

A lightweight system that uses cloud-based satellite imagery analytics and basic ML to highlight prospective zones and structural features across a license area. Geologists can overlay these AI-derived targets on existing GIS layers to prioritize where to focus fieldwork and drilling. This validates the value of AI-driven perception without touching core operational systems.

Architecture

Rendering architecture...

Key Challenges

  • Limited labeled data for training prospectivity models
  • Risk of overinterpreting coarse-resolution satellite signals
  • Aligning AI outputs with existing GIS workflows and formats
  • Managing expectations about accuracy at this early stage

Vendors at This Level

Planet LabsDescartes Labs

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Market Intelligence

Technologies

Technologies commonly used in AI-Driven Geological Exploration Suite implementations:

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Real-World Use Cases