Counting a pot of gold: A till golden standard (AuGC-1)

Boivin, J. A.; Bèdard, L. Paul; Longuépée, Hugues

doi:10.1016/j.gexplo.2021.106821

Journal of Geochemical Exploration

2021

DOI: 10.1016/j.gexplo.2021.106821

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Counting a pot of gold: A till golden standard (AuGC-1)

J. A. Boivin

L. Paul Bèdard

Hugues Longuépée

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Cited by 3 publications

References 39 publications

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Automated Gold Grain Counting. Part 1: Why Counts Matter!

Girard¹,

Tremblay²,

Néron³

et al. 2021

Minerals

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The quantitative and qualitative assessment of gold grains from samples of glacial till is a well-established method for exploring gold deposits hidden under glaciated cover. This method, which is widely used in the industry and has resulted in numerous successes in locating gold deposits in glaciated terrain, is still based on artisanal gravity separation techniques and visual identification. However, being artisanal, it is limited by inconsistent recoveries and difficulties associated with visually identifying the predominantly small gold grains. These limitations hinder its capacity to decipher subtle or complex signals. To improve detection limits through the recovery of small gold grains, a new approach has recently been introduced into the industry, which is commercially referred to as the “ARTGold” procedure. This procedure involves the use of an optimized miniature sluice box coupled with an automated scanning electron microscopy routine. The capabilities of this improved method were highlighted in this study by comparing till surveys conducted around the Borden gold deposit (Ontario, Canada) using the conventional and improved methods at both local and regional scales. Relative to that with the conventional approach, the improved method increased the recovery of gold grains from samples (regional and down-ice mineralization) by almost one order of magnitude. (regional and down-ice mineralization), dominantly in regard of the small size fractions. Increasing the counts in low-abundance regional samples allows for a better discrimination between background signals and significant dispersions. The described method offers an alternative for improving the characterization of gold dispersal in glaciated terrain and related gold deposit footprints.

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Automated Gold Grain Counting. Part 1: Why Counts Matter!

Girard¹,

Tremblay²,

Néron³

et al. 2021

Minerals

View full text Add to dashboard Cite

show abstract

Automated Gold Grain Counting. Part 2: What a Gold Grain Size and Shape Can Tell!

Girard¹,

Tremblay²,

Néron³

et al. 2021

Minerals

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Glacial drift exploration methods are well established and widely used by mineral industry exploring for blind deposit in northern territories, and rely on the dispersion of mineral or chemical signal in sediments derived from an eroded mineralized source. Gold grains themselves are the prime indicator minerals to be used for the detection of blind gold deposits. Surprisingly, very little attention has been dedicated to the information that size and shape of gold grain can provide, other than a simple shape classification based on modification affecting the grains that are induced in the course of sediment transport. With the advent of automated scanning electron microscope (SEM)-based gold grain detection, high magnification backscattered electron images of each grain are routinely acquired, which can be used for accurate size measurement and shape analysis. A library with 88,613 gold grain images has been accumulated from various glacial sediment surveys on the Canadian Shield and used to detect trends in grains size and shape. A series of conclusions are drawn: (1) grain size distribution is consistent among various surveys and areas, (2) there is no measurable fine-grained gold loss due to natural elutriation in ablation or reworked till, or during the course of reverse circulation drilling, (3) there is no grain size sorting during glacial transport, severing small grains from large ones, (4) shape modification induced by transport is highly dependent on grain size and original shapes, and (5) the use of grain shape inherited from neighboring minerals in the source rocks is a useful feature when assessing deposit types and developing exploration strategies.

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Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

et al. 2022

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A multitude of applications in engineering, ore processing, mineral exploration, and environmental science require grain recognition and the counting of minerals. Typically, this task is performed manually with the drawback of monopolizing both time and resources. Moreover, it requires highly trained personnel with a wealth of knowledge and equipment, such as scanning electron microscopes and optical microscopes. Advances in machine learning and deep learning make it possible to envision the automation of many complex tasks in various fields of science at an accuracy equal to human performance, thereby, avoiding placing human resources into tedious and repetitive tasks, improving time efficiency, and lowering costs. Here, we develop deep-learning algorithms to automate the recognition of minerals directly from the grains captured from optical microscopes. Building upon our previous work and applying state-of-the-art technology, we modify a superpixel segmentation method to prepare data for the deep-learning algorithms. We compare two residual network architectures (ResNet 1 and ResNet 2) for the classification and identification processes. We achieve a validation accuracy of 90.5% using the ResNet 2 architecture with 47 layers. Our approach produces an effective application of deep learning to automate mineral recognition and counting from grains while also achieving a better recognition rate than reported thus far in the literature for this process and other well-known, deep-learning-based models, including AlexNet, GoogleNet, and LeNet.

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Counting a pot of gold: A till golden standard (AuGC-1)

Cited by 3 publications

References 39 publications

Automated Gold Grain Counting. Part 1: Why Counts Matter!

Automated Gold Grain Counting. Part 1: Why Counts Matter!

Automated Gold Grain Counting. Part 2: What a Gold Grain Size and Shape Can Tell!

Deep-Learning-Based Automatic Mineral Grain Segmentation and Recognition

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