Introduction
The Data Quality Score aims to enhance your mine planning and decision-making. Firstly, we've introduced an overarching Data Quality Score as a new column on the Block Models section page, offering a comprehensive overall view of data quality within the Stockpile block model file. Secondly, the Data Quality Score has been incorporated for each block within 3D block model files. These scores empower geologists and mine planners to assess data quality efficiently, aiding in identifying areas with lower-quality data. This information is invaluable for reducing uncertainty in plant feed planning, enabling more informed and reliable decisions in mine operations.
Challenge
The Stockpile and Inventory Optimization Application relies on GPS coordinates and material properties, often provided by the Fleet Management System (FMS), for precise allocation of dumps, reclaim events, and stockpile modeling. Additionally, data can be sourced from mine site control sheets, summarizing events at specific timestamps, including average properties, total mass, and location labels for untracked equipment. Historical data is also used for long-term stockpile modeling. In cases where detailed event data is lacking, weighted average grades (WAM) are calculated for modeling purposes. However, these diverse data sources and practices can lead to challenges such as missing properties and incomplete GPS coordinates, impacting the quality of stockpile block models. Assessing data quality within 3D block models can be challenging for geologists and mine planners which can lead to uncertainties in plant feed planning and decision-making, potentially causing costly inaccuracies in mining operations.
Solution
To address the aformentioned challenges and ensure accurate modeling, the Data Quality Score was introduced. It evaluates data quality on a scale from 0% (lower quality) to 100% (highest quality) at both the over all model and block levels. Factors considered include GPS completeness, material property reliability, and data granularity, aiding geologists and mine planners in making informed decisions for precise mine planning.
How it works?
The Data Quality Score functions at both the model level and the block level, providing a comprehensive assessment of data quality.
Model-Level Evaluation: At the model level, the Data Quality Score provides a comprehensive assessment of data quality for the entire stockpile block model file, simplifying intricate data into a single, user-friendly score. This score offers users an instant and dependable gauge of data quality. Accessing the Data Quality Score is straightforward; navigate to the Block Models section page, where you'll find a dedicated Data Quality Score column in both the Latest and Historical block models tabs. This column displays the model-level Data Quality Score, allowing users to quickly and easily assess overall data quality (see image below).
For more detailed information about how the Data Quality Score is calculated for each particular block model file, click on the score of your interest, and it will open a window with the metrics breakdown and due explanation (see image below for reference).
2. Block-Level Assessment: Beyond the model level, the Data Quality Score assesses individual blocks within 3D block model files. For each block, it considers factors like the completeness of GPS coordinates, the reliability of material properties, and the granularity of data, similar to the model-level evaluation but at a block level.
Now, when you download the stockpile block model files, you'll find a column called "data_quality_score." This column enables geologists and mine planners to identify areas with lower-quality data, aiding in pinpointing and addressing data quality concerns (See image below for reference).
Value
Streamlined QA/QC: The Data Quality Score simplifies the quality assessment process, allowing geologists to efficiently identify and address data quality issues before utilizing blockmodels.
Enhanced Accuracy: Geologists can rely on the Data Quality Score to pinpoint areas with lower-quality data, ensuring a more accurate and reliable foundation for their work.
Confidence in Decision-Making: By reducing uncertainties and providing a comprehensive view of data quality, the Data Quality Score enables geologists to make more confident decisions in mine planning and operations.
Conclusion
The Data Quality Score is a powerful tool for enhancing your mine planning and decision-making. If you have any doubts or need assistance, please contact our intellisense.io support team. Your feedback is invaluable as we refine this beta feature to better serve your needs. Discover the value of streamlined QA/QC work, enhanced accuracy, and confident decision-making with the Data Quality Score now inserted in our stockpile blockmodel files.