SIO App’s Comprehensive Blockmodel Guide
The primary output of the SIO App is the 3D stockpile blockmodel file. While its central role is to offer detailed insights into property grades, it also incorporates various other features, including categorical variables. Crucially, these properties can be customized to be included or excluded based on individual site preferences. In general, the blockmodels can track as many variables as are available by the site with no constraints. Below is a detailed breakdown:
1. Core Purpose:
Property Grades: The backbone of the blockmodel, this delivers comprehensive insights into the different property grades tracked by the mine site present witin the stockpile.
2. DataMine Compatibility:
IJK, XC, YC, ZC, XINC, YINC, ZINC: These parameters are optimized for seamless integration with the DataMine system.
XMORIG, YMORIG, ZMORIG, NX, NY, NZ: Additional variables fine-tuned specifically for DataMine.
3. Data Quality Metrics:
fms_scan_mismatch: For LDA conformations, this denotes the fraction of material that wasn't aligned with the scan.
gps_fill_frac: Represents the fraction of material in a block positioned using the Smart GPS filling.
prop_invalid_frac: Highlights the fraction of material with missing property values.
prop_from_init_frac: Pinpoints the fraction of material that originates from the initial provided dataset.
data_quality_score: The Data Quality (DQ) score is a rating between 0% (poor data) and 100% (excellent data) that measures the quality of information used to build the stockpile models. It considers factors like missing GPS data, invalid properties, and initial WAM data, with each factor weighted by its significance.
4. Spatial Variables
Volume (m3): Measures the block's volume in cubic meters.
Fill: Specifies the proportion of the block filled with material.
5. Material Age
Age_weeks: Average age of material in the block in weeks.
6. Stockpile Segmentation
Subregion: Determines which specific zone or segment within the stockpile the block is associated with.
7. Categorical Variables and Material Provenance:
The SIO App's blockmodel is versatile in its ability to handle categorical variables, allowing you to capture important data beyond property grades. These categorical variables include information such as rock types, lithology, and more, which can be invaluable for a deeper understanding of your stockpile.
Provenance: The Stockpile and Inventory Optimization App employs FMS (Fleet Management System) location labels as categorical variables to trace the origins of materials. Each label corresponds to the source of a load. In cases where blocks contain materials from various sources, the app automatically assigns the label of the most dominant source to each block. This system enables users to quickly identify the primary source of material in each block.
In essence, the blockmodel can flexibly accommodate a wide range of categorical variables, providing comprehensive insights into your stockpile's composition and origin.