Achieving a precise and accurate block model that reflects geological reality is a persistent challenge, particularly in underground environments where data variability and spatial inconsistencies are common. GroundedAI (formerly RockMass) addresses this by enabling systematic data capture at the blast face. By simplifying complex geotechnical information and delivering high-quality inputs, it supports more reliable resource estimation and improved ore recovery.
Industry Challenge
In underground mining, manually collected geological data often results in spatial inaccuracies. Sample locations are frequently generalized and cannot be subjected to proper QA/QC. Blast face maps may lack georeferencing, and hand-drawn interpretations are often improperly scaled. These limitations compromise the quality of the block model. Inconsistent annotations and the absence of digitized records make it difficult to connect key geological features, leading to incomplete interpretations. Without a solution that captures 3D mapping data systematically, enables precise georeferencing, and aggregates inputs at the blast round level, accurate and dependable block modeling remains difficult to achieve.
Our Approach
GroundedAI empowers production geology and mine planning teams with a structured workflow that transforms raw point cloud data into high-quality, georeferenced geological models. By simplifying and organizing complex structural and geological datasets, GroundedAI ensures seamless integration into resource estimation and block modeling software.
Workflow Example
Key Benefits
1 - Maximized ore recovery
Achieved through precise delineation of ore and waste boundaries at the blast face.
2 - Improved resource estimation accuracy
Enabled by consistent, high-quality data integration that supports reliable block model updates.
3 - Enhanced operational profitability
Driven by optimized ore recovery and efficient data workflows that minimize waste extraction costs.
Make the underground visible
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