Application of classification trees for improving optical identification of common opaque minerals

Domínguez-Olmedo, Juan L.; Toscano, Manuel; Mata, Jacinto

doi:10.1016/j.cageo.2020.104480

Cited by 6 publications

(10 citation statements)

References 29 publications

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“…Reflected light optical microscopy (RLOM) images -CNN [80] μCT images -CNN [72] Multispectral images -LDA [70]; FF-ANN [70] Reflectance spectra -Bayes nets [100] X-ray spectrum data PCA [95] ANN [95] X-ray microtomography scans -Fuzzy inference system (FIS) [74] Images of microscopic rock thin section (RGB pixels) -k-NN [77]; DT [77] Optical identification of minerals Mineral properties such as color, hardness, pleochroism, anisotropism, and internal reflections Cramer's Vand Pearson correlation coefficient (PCC) [75] DT [75] Prediction of concentrate yield and modal mineralogy Bulk chemistry data from the mining company open pit database -ANN [79] Predicting rock type and mine face, detecting hydrothermal alteration Physical properties of rocks -SVM [59] Hyperspectral data -GP [53,86]; SVM [86]; SAM [86] Multi-element geochemistry K-means++ [93] R F [ 93] Images of the rocks -ANN [94] Reducing noise in hyperspectral data…”

Section: Application Dataset Feature Engineering Methods ML Techniquementioning

confidence: 99%

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

2022

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Section: Application Dataset Feature Engineering Methods ML Techniquementioning

confidence: 99%

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

2022

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“…Reflected light optical microscopy (RLOM) images -CNN [80] µCT images -CNN [72] Multispectral images -LDA [70]; FF-ANN [70] Reflectance spectra -Bayes nets [100] X-ray spectrum data PCA [95] ANN [95] X-ray microtomography scans -Fuzzy inference system (FIS) [74] Images of microscopic rock thin section (RGB pixels) -k-NN [77]; DT [77] Optical identification of minerals Mineral properties such as color, hardness, pleochroism, anisotropism, and internal reflections Cramer's Vand Pearson correlation coefficient (PCC) [75] DT [75] For the sake of providing a general insight into the scope of the reviewed papers, Figure 6 illustrates the word frequency map of their titles, where the more popular words are represented with a bigger font size. As depicted, aside from the common keywords, namely using, machine, learning, and mineral, we can generally infer the main trends discussed in the previous sections considering the relatively high frequency of the words hyperspectral, mapping, and classification.…”

Section: Application Dataset Feature Engineering Methods ML Techniquementioning

confidence: 99%

“…The observation of optical properties of a mineral in a polarized microscope rotation stage is a commonly used method for mineral type classification. This task can be automated by the application of digital image processing techniques and AI technologies [75][76][77][78].…”

Section: Problems In the Selected Studies Addressed Using ML Techniquesmentioning

confidence: 99%

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

2021

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Machine learning is a subcategory of artificial intelligence, which aims to make computers capable of solving complex problems without being explicitly programmed. Availability of large datasets, development of effective algorithms, and access to the powerful computers have resulted in the unprecedented success of machine learning in recent years. This powerful tool has been employed in a plethora of science and engineering domains including mining and minerals industry. Considering the ever-increasing global demand for raw materials, complexities of the geological structure of ore deposits, and decreasing ore grade, high-quality and extensive mineralogical information is required. Comprehensive analyses of such invaluable information call for advanced and powerful techniques including machine learning. This paper presents a systematic review of the efforts that have been dedicated to the development of machine learning-based solutions for better utilizing mineralogical data in mining and mineral studies. To that end, we investigate the main reasons behind the superiority of machine learning in the relevant literature, machine learning algorithms that have been deployed, input data, concerned outputs, as well as the general trends in the subject area.

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“…It can simplify and guide the evaluation of these properties to achieve appropriate mineral identification. [ 84 ]…”

Section: Single‐modal Recognition Of Mineral/rock Datamentioning

confidence: 99%

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

Zhang,

Yang,

Sun

et al. 2023

Advanced Intelligent Systems

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Artificial intelligence promotes the development of mineral/rock automatic recognition technology to reduce labor costs and reliance on personal experience. With instrument digitization, image recognition has played an increasingly important role. This review focuses on the image sensing system applied to mineral/rock identification and redivides this system into a sensing module, an imaging module, and an information processing module. The sensing and imaging mechanisms are summarized via technologies of sensing, imaging, and display. The information processing is classified into single‐modal or multimodal recognition. This review analyzes the limitations of single‐modal recognition and discusses the advantages and significance of multimodal recognition. The challenges of current mineral/rock identification are concluded, such as public database, multimodal learning, and portable sensing system. The interdisciplinary sensing technologies are discussed, and application feasibility is explored. This review aims to provide a reference for the research community in automatic mineral/rock identification and sensing system optimization.

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Application of classification trees for improving optical identification of common opaque minerals

Cited by 6 publications

References 29 publications

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

Value of Mineralogical Monitoring for the Mining and Minerals Industry In memory of Prof. Dr. Herbert Pöllmann

A Systematic Review on the Application of Machine Learning in Exploiting Mineralogical Data in Mining and Mineral Industry

Application of Image Sensing System in Mineral/Rock Identification: Sensing Mode and Information Process

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