A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion

Acosta, Isabel Cecilia Contreras; Khodadadzadeh, Mahdi; Tuşa, Laura; Ghamisi, Pedram; Gloaguen, Richard

doi:10.1109/jstars.2019.2924292

Cited by 83 publications

(62 citation statements)

References 39 publications

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“…For the proposed approach, the SEM-MLA data is upscaled by adopting a re-sampling procedure. The two-dimensional SEM-MLA mineral map with high spatial resolution is transformed to a three-dimensional mineral abundance map with the lower spatial resolution of the hyperspectral data [7]. The third dimension consists of the relative abundance of each mineral present in each SEM-MLA map re-sampled to the hyperspectral pixel size (Figure 3).…”

Section: Hsi-sem-mla Data Integrationmentioning

confidence: 99%

“…Hyperspectral imaging is currently being used in the mining and exploration industries as an alternative tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method to obtain mineralogical information [4][5][6][7]. Typical hyperspectral core imaging systems can deliver data from a whole core tray (which holds approximately 5 m of core) in a matter of seconds.…”

Section: Introductionmentioning

confidence: 99%

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Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

et al. 2020

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Due to the extensive drilling performed every year in exploration campaigns for the discovery and evaluation of ore deposits, drill-core mapping is becoming an essential step. While valuable mineralogical information is extracted during core logging by on-site geologists, the process is time consuming and dependent on the observer and individual background. Hyperspectral short-wave infrared (SWIR) data is used in the mining industry as a tool to complement traditional logging techniques and to provide a rapid and non-invasive analytical method for mineralogical characterization. Additionally, Scanning Electron Microscopy-based image analyses using a Mineral Liberation Analyser (SEM-MLA) provide exhaustive high-resolution mineralogical maps, but can only be performed on small areas of the drill-cores. We propose to use machine learning algorithms to combine the two data types and upscale the quantitative SEM-MLA mineralogical data to drill-core scale. This way, quasi-quantitative maps over entire drill-core samples are obtained. Our upscaling approach increases result transparency and reproducibility by employing physical-based data acquisition (hyperspectral imaging) combined with mathematical models (machine learning). The procedure is tested on 5 drill-core samples with varying training data using random forests, support vector machines and neural network regression models. The obtained mineral abundance maps are further used for the extraction of mineralogical parameters such as mineral association.

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Section: Hsi-sem-mla Data Integrationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

et al. 2020

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“…A hyperspectral image [44] was acquired from a drill core sample by using a SisuRock drill core scanner, equipped with an AisaFenix VNIR-SWIR hyperspectral sensor. An RGB image of this drill core sample is shown in Figure 4a.…”

Section: Dataset 3: Drill Core Hyperspectral Datasetmentioning

confidence: 99%

“…The MLA image was resampled and co-registered with the hyperspectral image, after which ground truth fractional abundance maps of 373 pixels were generated. For more information about SEM-MLA and the generation of the groundtruth, we refer to [44]. Due to the complexity of the sample and the resolution of the dataset, there are no pure pixels composed of only one mineral.…”

Section: Dataset 3: Drill Core Hyperspectral Datasetmentioning

confidence: 99%

A Supervised Method for Nonlinear Hyperspectral Unmixing

et al. 2019

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Due to the complex interaction of light with the Earth’s surface, reflectance spectra can be described as highly nonlinear mixtures of the reflectances of the material constituents occurring in a given resolution cell of hyperspectral data. Our aim is to estimate the fractional abundance maps of the materials from the nonlinear hyperspectral data. The main disadvantage of using nonlinear mixing models is that the model parameters are not properly interpretable in terms of fractional abundances. Moreover, not all spectra of a hyperspectral dataset necessarily follow the same particular mixing model. In this work, we present a supervised method for nonlinear spectral unmixing. The method learns a mapping from a true hyperspectral dataset to corresponding linear spectra, composed of the same fractional abundances. A simple linear unmixing then reveals the fractional abundances. To learn this mapping, ground truth information is required, in the form of actual spectra and corresponding fractional abundances, along with spectra of the pure materials, obtained from a spectral library or available in the dataset. Three methods are presented for learning nonlinear mapping, based on Gaussian processes, kernel ridge regression, and feedforward neural networks. Experimental results conducted on an artificial dataset, a data set obtained by ray tracing, and a drill core hyperspectral dataset shows that this novel methodology is very promising.

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“…HSI is an emerging platform that combines traditional spectral and imaging techniques to obtain spectral and spatial information from samples [9]. Hyperspectral technology is widely used in geology and minerals, atmospheric sciences, oceans, agriculture, industrial production, and other fields [10][11][12]. Due to HSI information with the large spectral information, large frequency band, and high redundancy, the information processing of HSI is difficult.…”

Section: Introductionmentioning

confidence: 99%

Predicting the moisture content of Daqu with hyperspectral imaging

Chen

Tian

et al. 2020

International Journal of Food Engineering

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Daqu, a Chinese liquor fermentation starter, contains all kinds of microorganisms and enzymes for Chinese liquor fermentation. The moisture content of Daqu significantly influence on the reproduction of microorganisms in Daqu. This work presents for the first time that determination of moisture content of Daqu with hyperspectral imaging. The characteristic spectrum of water is extracted based on comparative experiments with varying moisture content. The molds based on the full bands and feature bands were established by the support vector regression (SVR) method, which is used to predict moisture content of Daqu during fermentation process. The performance of the model based on the feature bands (R2 = 0.9870, root mean square error (RMSE) = 0.0091) is comparable to the full bands and the dimensions of the spectral information were significantly reduced. This work presents a novel, rapid and nondestructive approach for detecting the moisture content in Daqu and lays a foundation for the application of hyperspectral imaging.

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A Machine Learning Framework for Drill-Core Mineral Mapping Using Hyperspectral and High-Resolution Mineralogical Data Fusion

Cited by 83 publications

References 39 publications

Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

Drill-Core Mineral Abundance Estimation Using Hyperspectral and High-Resolution Mineralogical Data

A Supervised Method for Nonlinear Hyperspectral Unmixing

Predicting the moisture content of Daqu with hyperspectral imaging

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