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Block Model Conformation Method: Local Density Adjustment (LDA)
Block Model Conformation Method: Local Density Adjustment (LDA)

Description of the operational mechanism and appropriate use cases for the Local Density Adjustment (LDA) conformation approach.

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Written by René
Updated over a week ago

What is the LDA Conformation Method?

The Local Density Adjustment (LDA) blockmodel conformation method is an advanced feature offered by the SIO App for calibrating stockpile blockmodels using scan surveys. With a strong focus on data quality, the LDA method precisely aligns stockpile blockmodels with the latest scan survey data, using the previous conformation as a reference point. It focuses on areas indicated by FMS data and utilizes data quality flags to address discrepancies where material is missing in the model but present in the scan. By adjusting the density of model columns within specified tolerance limits, the model matches the scan surface to reflect the actual topography of the stockpile.

Key Steps to Understand the LDA Conformation Method

To grasp the essence of the LDA Conformation Method, consider these key steps:

  1. Alignment with Scan Surveys:

    • LDA aligns stockpile blockmodels with the latest scan survey data, using the previous conformation as a reference point. This ensures that your block models accurately represent the actual topography of the stockpile.

  2. Data Quality Focus:

    • The method focuses on areas indicated by FMS data and utilizes data quality flags to address discrepancies where material is missing in the model but present in the scan. This meticulous attention to data quality makes discrepancies visible and aids in their identification and resolution.

  3. Adjusting Model Density:

    • By adjusting the density of model columns within specified tolerance limits, LDA minimizes unnecessary changes while making sure the model closely matches the scan surface. This precision reduces the need for extensive model adjustments.

  4. Adaptability:

    • LDA is versatile and adapts to different mining environments. It is ideal for sites equipped with high-precision GPS (HPGPS) systems, such as autonomous fleets and excavators with machine guidance systems. However, it remains adaptable to conventional GPS when data quality meets standards, ensuring flexibility in model calibration.

When to use the LDA Conformation Method?

The LDA Conformation Method is particularly beneficial in the following scenarios:

  • Sites Equipped with High-Precision GPS (HPGPS): The LDA method is exceptionally valuable at sites equipped with HPGPS, where data accuracy reaches exceptional levels (less than 1-meter uncertainty). In these cases, the LDA method minimizes model adjustments, ensuring that the model closely matches the scan survey data.

  • Prioritizing Data Quality: When a site places a high priority on having top-quality data to ensure the accuracy and reliability of block models, the LDA method is the recommended choice. It proactively identifies data quality issues, making them visible and aiding in their resolution.

Benefits of the LDA Conformation Method

  1. Improved Decision-Making: By aligning block models with the latest scan survey data, the LDA method enhances the accuracy of modeling. This precision alignment ensures that block models closely represent on-site conditions. As a result, mining operations can make more informed decisions, leading to optimized resource allocation and planning.

  2. Enhanced Data Quality Assurance: The LDA method places a strong focus on data quality. It proactively identifies areas with data quality issues, such as discrepancies between the block model and actual topography. By making these issues visible, mining teams can take corrective actions, reducing the risk of inaccuracies and improving overall data reliability.

  3. Visual Clarity: One of the key advantages of the LDA method is its ability to visually expose discrepancies between the block model and scan survey data. This visual clarity aids in the identification and resolution of data quality issues. Minimizing these discrepancies leads to more reliable and actionable data for mining operations.

How to implement the LDA Conformation Method

Please reach out IntelliSense.io support team to configure the conformation method of your choice.

Other Model Conformation Methods

In addition to the LDA Conformation Method, the Stockpile and Inventory Application offers various other conformation methods such as the Relative Redistribution of Batches (RRB), Subregions Redistribution of Batches (SRB), and the Hybrid GPS-Scan Driven Conformation approach. These conformation methods are designed to cater to different site requirements and scenarios, allowing you to choose the most suitable approach.
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  • Note: Each conformation method can be configured to align with your stockpile modeling needs. Whether you prioritize topography precise alignment, data quality, material mix preservation, or other factors, our application allows you to tailor the conformation method accordingly.

Conclusion

The Local Density Adjustment (LDA) blockmodel conformation method is a powerful tool for enhancing the accuracy and reliability of your stockpile blockmodels. Whether you have High-Precision GPS (HPGPS) or conventional GPS, LDA adapts to your needs. It minimizes model adjustments and proactively identifies data quality issues, ensuring your blockmodels accurately reflect site conditions. Make informed decisions, prioritize data quality, and elevate your mining operations with LDA.


Explore our range of conformation methods tailored to meet your site-specific requirements.

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