Geostatistics

The Importance of Hyperparameter Optimization in Machine Learning Models Applied to Mining

The Importance of Hyperparameter Optimization in Machine Learning Models Applied to Mining

In recent years, the use of Machine Learning (ML) in mining has grown significantly, particularly in applications related to grade prediction, metallurgical recovery, geological classification, and geometallurgical variables such as hardness, energy consumption, and throughput. However, one of the most important — and at the same time most underestimated — aspects in

Integration of Different Data Supports in Short-Term Estimation

Integration of Different Data Supports in Short-Term Estimation

The problem: data of different quality and support In mining projects, it is common to simultaneously have two or more sources of geochemical information that differ in both sampling density and physical support: exploration drillholes (DDH or RC), collected throughout the life of the project with relatively wide spacing, and

Beyond Kriging: the Role of Conditional Simulations in Resource Evaluation

Beyond Kriging: the Role of Conditional Simulations in Resource Evaluation

In recent years, conditional simulations have been gaining increasing importance in Mineral Resource evaluation and are being more frequently required in projects and operating mines, due to their multiple uses. However, their greater complexity means that they are not yet used across the board in the industry. In the following

Additive and Non-Additive Variables III: Reviewing Additivity and Linearity Properties of Percentage-Type Concentrations

Additive and Non-Additive Variables III: Reviewing Additivity and Linearity Properties of Percentage-Type Concentrations

This is the final entry related to additive and non-additive variables. Remember that you can find information on non-additive geotechnical and metallurgical variables in our previous post. Are Cu (%) concentrations additive and linear? Below, we will review percentage-type concentration variables defined by: Concentration (X) % = 100 *(masa)X/(masa)Tot, that

Additive and Non-Additive Variables II: Examples of non-additive geotechnical and metallurgical variables

Additive and Non-Additive Variables II: Examples of non-additive geotechnical and metallurgical variables

Following our series of posts on additive and non-additive variables, today we will discuss the case of geotechnical and metallurgical ones. Remember that you can find the basic definitions and properties in our previous post. Example of geomechanical variables In the following examples, the intention is not to address the

Additive and Non-Additive Variables I: Definitions and Properties

Additive and Non-Additive Variables I: Definitions and Properties

When performing an estimation or simulation of a new variable, it is important to ask about its additive property, its linearity, and understand its definition. If the variable is not additive, this knowledge allows us to evaluate appropriate methodologies and techniques for estimating it — for example, by simplifying, referring to

Relevant Aspects to Consider During a Mineral Resource Model

Relevant Aspects to Consider During a Mineral Resource Model

Today, I want to introduce and discuss the basic concepts that should be understood when developing a Mineral Resource model. Since this post will cover general topics, it's intended for an audience just starting out in this field, so you won’t find in-depth or complex information here.