Process Mineralogy Today

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Working with High-Dimensional Data Part 4: Classifying Unknown Samples using Machine Learning Principles

In the previous articles in this series (part 1, part 2, and part 3) we’ve been performing analyses on an example high-dimensional geochemical data set from a resource feasibility study with the goal of developing a geometallurgical domain model, which could be used for sample selection for more detailed characterisation work and practical mine planning. In this article we continue with this example and take a closer look at how the geometallurgical domains from part 3 guide the grouping of unclassified samples collected from the same resource. ...

Working with High-Dimensional Data Part 3: Geospatial Mapping and Mine Planning

 

In part 1 of this introductory series about working with high-dimensional data we looked at dimensionality reduction to allow the visualisation of complex data. Part 2 of the series explored the K-means clustering method as a technique to classify samples according to their geochemical characteristics. In this article we place the classified samples back into their geospatial context and use the spatial relationships between the clusters to define a behaviour profile, which is aimed at guiding critical mine planning decisions. ...

Working with High-Dimensional Data Part 2: Classification by Cluster Analysis

In part 1 of this introductory series on working with high-dimensional data we determined that cluster analysis is a commonly used method to perform more reliable and scalable classification of large data sets. In the case of high-dimensional data such as geochemical assays or SEM-EDS mineralogy and texture analyses, dimensionality reduction enables visualisation of the raw data and provides a framework to assess the quality of the cluster analysis results. In this article we take a closer look at the K-means clustering method to form a better understanding of its strengths and weaknesses. ...

Working with High-Dimensional Data, Part 1: Dimensionality Reduction

Mineral exploration, mining, ore processing, and, more generally, earth science research, involves the collection of large and complex data sets where single sampling points often consist of multiple dimensions. For example, the analysis results of a single field specimen collected during exploration, or a sample from a mineral processing stream, contains the proportions of multiple elements and minerals. A common data analysis goal is to group samples according to their chemical and mineralogical characteristics or according to the manner in which they behave during metallurgical testing. In this series of articles we briefly look at the techniques that enable the grouping and assessment of high-dimensionsional data. ...

Considerations with QEMSCAN Grain Size Estimation

Most SEM-EDS systems have the ability to compute grain size. Not all systems use the same computational methods and we advise that you familiarise yourself with the details of your system of choice. In the case of QEMSCAN the presence of fine-grained inclusions in the mineral of interest (e.g.: a major ore-forming mineral like chalcopyrite, or key minerals in sedimentary systems such as quartz) has an affect on the computed mineral grain size, therefore, care must be taken when using automated grain size calculations.   ...

Linking Sample Prep with Particle Stats and Data Quality

 

 

In last week’s article we spent some time evaluating particle statistics using SEM-EDS data to demonstrate the importance of the number of particles measured. As I analysed and plotted the data it dawned on me just how critical sample preparation really is. Of course we know that sample preparation is important. It is fairly intuitive that the particle population has to be distributed and oriented randomly within the sample block volume and the exposed block section to achieve a representative result, but this was the first time I could visualise why. In this article we will spend some time evaluating the implications of non-random particle distributions and the practical implications of the number of particles measured. ...

Evaluation of SEM-EDS Particle Count Statistics

In our last article I re-introduced the concept of using statistics to determine if enough particle sections have been measured to produce a representative result in SEM-EDS analysis. This week I thought I’d give an example of a SEM-EDS data set to explore how the particle count impacts results and data quality. Let’s start by looking at figure 1a, which shows the cumulative quartz volume percent as a function of the number of particles measured. The data are presented in the order of particles measured. ...

Have you measured enough particles?

One of the key questions about SEM-EDS data is whether or not you’ve measured enough particle sections to produce a representative result. The critical part to remember is that SEM-EDS data are collected on particle sections exposed in a sample block, which is unlike other bulk analysis techniques such as XRF or XRD. It follows that the data from any one particle section is inherently biased and not a true representation of the character of the sample. Only a population of particle sections are able to provide accurate information. Adequate particle statistics are critical for applications such as operational mineralogy where liberation, grain size and mineral associations play a key role in mineral processing behaviour.  The question is, how many particle sections are enough? ...

The Applied Mineralogy User Community

The field of applied mineralogy has always had a basis in a strong community, this has led to to numerous developments that have benefited us all and at MinAssist we have been exploring ways to strengthen that community.

 

In the early stages of SEM-EDS commercialisation direct input from user groups was a strong driver in development of the technology.  However, over the past number of years this has fallen by the wayside. Through various conversations with former user group members it is clear that there is a continued desire for a way connecting users of mineralogy data.

 

MinAssist has developed a platform to facilitate our community and would like to invite you to help us build this by joining the beta test of the Applied Mineralogy User Community (AMUC). If you use data generated by techniques such as XRF, XRD, EPMA, SEM-EDS, ICPMS, optical microscopy, µXRF, Infra-red scanning, etc., the Applied Mineralogy User Community welcomes your membership. 

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THE IMPORTANCE OF MINERAL DEFINITIONS USED IN GENERATING QEMSCAN DATA

The most common approach to assessing the accuracy of QEMSCAN mineralogy results is to compare the measured assay with the mineralogy-computed chemical assay. In this method we assume the measured assay results are the correct values, which means we also assume that the sub-samples used for chemical assay and QEMSCAN analysis are identical, or equally representative, and that the SEM-EDS sample preparation procedure has not introduced any bias. However, the nature of QEMSCAN data means that is quite easy to achieve an acceptable reconciliation of less than 10 or 15% (depending on the element) with very different mineralogy results. ...