Non-regular Block Models
Highlights
This article explains how MiningMath advanced users can handle different types of non-regular block models and how they can adapt their data within the current software capabilities. It also presents practical workarounds for working with sub-block and percentual models, and how users can influence future features through direct contributions.
Context: Block Model Definitions and Practical Constraints
Defining a block model involves balancing geological precision, statistical reliability, and engineering feasibility. The block size is influenced by:
- Geology: Complex 3D bodies modeled from drillhole data that do not follow a block structure.
- Geostatistics: Requires a block size compatible with sample spacing, avoiding sizes too small or too large.
- Engineering (SMU – Smallest Mining Unit): Dictated by equipment selectivity. If block size matches SMU, selective mining within the block is not feasible.
Using blocks smaller than the SMU can lead to overestimations of value and unrealistic assumptions about selectivity and operational precision.
For more details about the different types of block models, access the full article on Types of Block Models in our Knowledge Base.
MiningMath Capabilities
Native Support
MiningMath currently supports only regular models natively. While other models such as sub-blocked and percentual are widely accepted in the industry, supporting them natively requires adaptations at the algorithmic level. Rather than attempting partial or unstable support, MiningMath has prioritized a stable foundation for regular models while offering flexible workarounds for others. These workarounds already enable meaningful results for advanced users and lay the groundwork for future native support as demand and resources evolve.
Advanced Use: Simulating Percentual and Sub-block Models
These models can be simulated by:
- Adjusting block density to reflect only the mass of ore. This involves editing the density variable of each block to represent the fraction of ore inside it. For example, if a block contains 70% ore and 30% waste, the density can be recalculated to reflect only the ore mass.
- Calculating economic values based on predefined proportions. This step requires manually defining economic values that represent the block’s material mix. Users can create a custom economic value field that combines the contribution of each material type within a block.
- Using sum variables to track full mass, ore, and waste volumes. By creating separate sum variables, users can differentiate between total block mass, ore-only mass, and waste, allowing for reporting and constraint control.
For sub-blocked models, users may:
- Aggregate sub-blocks into parent blocks and assume total processing. This means treating the entire parent block as either ore or waste based on dominant material or economic value.
- Convert sub-block information into percentual logic and apply the same method above. Users can collapse sub-block content into proportional representations at the parent block level, then follow the percentual model approach for density, value, and restriction handling.
These adjustments allow MiningMath to optimize based on simplified yet controlled approximations.
Help Us Shape the Future
Native handling of percentual and sub-blocked formats is part of our vision, but its implementation depends on real demand and dedicated investment. If you want to accelerate this roadmap, your input can make a difference:
- Submit public scripts for Labs inclusion (with basic stability);
- Share anonymized datasets with sample inputs and outputs;
- Provide technical descriptions, use cases, or structured feedback.
- Providing technical documentation (including pseudocode and interface suggestions).
Even small contributions help guide our priorities and improve feature delivery timelines.
Want to participate?
Share your use case or materials with our team and help us shape what comes next.
The more detailed your contribution, the faster our evaluation and implementation process. MiningMath prioritizes features backed by real use cases and clear technical value.
