# STATISTICAL VALIDATION with GEMS

The importance of the Validation of the Block Model Estimation with GEOVIA GEMS: STATISTICAL VALIDATION.

When estimating a block model, no matter what interpolation method is chosen, it is essential to validate the obtained results in order to ensure the estimation process quality according to the current industry standards.

By validating the block model, we look for:

• That the estimation be globally unbiased
• Minimize local bias
• Keep the estimation smoothing at a reasonably acceptable level

There are numerous methods to do this and through upcoming blogs we will discuss these three (3):

1. Graphical validation.
2. Statistical validation.
3. Swath Plot validation.

Next, we will explain the method of Statistical Validation:

STATISTICAL VALIDATION OF THE BLOCK MODEL ESTIMATION

Objective:
Validate that the estimation is globally unbiased, that is to say, that the global mean of the estimation (blocks) is the same as the global mean of the “real” data (declustered composites).

How to do it?
Basic statistics are reported by domain for the estimated blocks and for the declustered composites; the global bias is calculated, as follows:

Where:

AV = Global mean of estimated blocks
BV = Global mean of declustered composites

Care to take into account at the execution:

• That the composites have the same support as the size of the blocks
• Perform the analysis only on the best estimated blocks, e.g., those that are candidates to measured and indicated resources
• That the composites have been previously declustered. It is suggested to use the nearest neighbour method (NN) as declustering method.
• That the analysis be performed separately by estimation domain

Expected Results:

• Current industry standards require that the global bias of the block model estimation is less than 5%.
• Global biases between 5 and 10% are considered questionable.
• Global biases greater than or equal to 10% are considered unacceptable.

Statistics of estimated and NN blocks are performed as shown below:

Finally, an example of the report is shown:

#### Maria Angelica Gonzalez

María-Angélica is a Mining Engineer with a Diploma in Geostatistics and she is a candidate for a Master of Science mention Geology. She is registered as a Competent Person with the Qualification Commission of Competencies in Resources and Mining Reserves of Chile. She started her professional career in 1999 and has worked in consulting and project operations for diverse types of mineral deposits, performing the geostatistical evaluation of elements of economic interest as well as geometallurgical and contaminant variables. She is experienced in metallic and non-metallic projects, such as copper, gold, silver, molybdenum, iron, zinc, lead, titanium sands, phosphates, carbonates, iodine and nitrates, both in America and in Africa. Her current position is Senior Services Manager of Dassault Systèmes Chile (GEOVIA), serving the Mining Industry in Latin America. She is in charge of both consulting and training as well as technical sales, mainly in the technical and administrative leadership of projects. Previously, she worked as Leader of Geology and Resources of the Consulting Group at AMEC. AMEC International Ingeniería y Construcción.