Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization

Tuşa, Laura; Andreani, Louis; Khodadadzadeh, Mahdi; Contreras, Cecilia; Ivascanu, Paul M.; Gloaguen, Richard; Gutzmer, Jens

doi:10.3390/min9020122

Cited by 30 publications

(22 citation statements)

References 40 publications

(48 reference statements)

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“…Most commonly, single or integrated sensors covering VNIR [11] and/or SWIR [12,13,14,15,16] are used, often in combination with RGB image data. Recent studies aim at automatic vein extraction directly from the HSI to allow an interpretation of veins and structural features based on their spectral and spatial characteristics [17]. To handle the large amount of data, machine learning approaches have recently been applied to drill core HSI.…”

Section: Introductionmentioning

confidence: 99%

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

Lorenz

Seidel

Ghamisi

et al. 2019

Sensors

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Rapid, efficient and reproducible drillcore logging is fundamental in mineral exploration. Drillcore mapping has evolved rapidly in the recent decade, especially with the advances in hyperspectral spectral imaging. A wide range of imaging sensors is now available, providing rapidly increasing spectral as well as spatial resolution and coverage. However, the fusion of data acquired with multiple sensors is challenging and usually not conducted operationally. We propose an innovative solution based on the recent developments made in machine learning to integrate such multi-sensor datasets. Image feature extraction using orthogonal total variation component analysis enables a strong reduction in dimensionality and memory size of each input dataset, while maintaining the majority of its spatial and spectral information. This is in particular advantageous for sensors with very high spatial and/or spectral resolution, which are otherwise difficult to jointly process due to their large data memory requirements during classification. The extracted features are not only bound to absorption features but recognize specific and relevant spatial or spectral patterns. We exemplify the workflow with data acquired with five commercially available hyperspectral sensors and a pair of RGB cameras. The robust and efficient spectral-spatial procedure is evaluated on a representative set of geological samples. We validate the process with independent and detailed mineralogical and spectral data. The suggested workflow provides a versatile solution for the integration of multi-source hyperspectral data in a diversity of geological applications. In this study, we show a straight-forward integration of visible/near-infrared (VNIR), short-wave infrared (SWIR) and long-wave infrared (LWIR) data for sensors with highly different spatial and spectral resolution that greatly improves drillcore mapping.

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Section: Introductionmentioning

confidence: 99%

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

Lorenz

Seidel

Ghamisi

et al. 2019

Sensors

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“…For testing the proposed methodology, 5 samples, labelled DC-1 to DC-5, from different locations within the Bolcana porphyry copper-gold system [23][24][25][26] were analyzed. Hyperspectral images were acquired on the halves cores after which thin sections were prepared from selected regions of interest and analyzed by SEM-MLA.…”

Section: Data Descriptionmentioning

confidence: 99%

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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“…The used pixel size was 3

m × 3

m and the threshold of quality for correct classification was set to 90%. Further details on the MLA experiments can be found in [ 13 ].…”

Section: Data Acquisition and Preprocessingmentioning

confidence: 99%

“…Along with the aforementioned applications in which hyperspectral imagery plays important roles, such sensors have been used intensively for mineral mapping and raw material characterization [ 9 , 10 , 11 ]. Satellite and airborne campaigns for large-scale regional mapping [ 12 ] and drillcore scanning for the characterization of underground deposits [ 13 ] are currently the most developed application-oriented fields of hyperspectral data for mineral resources. However, close-range terrestrial [ 14 ] and drone-borne measurements [ 15 ] are emerging and allow spatially and timely detailed mapping of outcrops and mines.…”

Section: Introductionmentioning

confidence: 99%

Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples

Rasti

Ghamisi

Seidel

et al. 2020

Sensors

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Geological objects are characterized by a high complexity inherent to a strong compositional variability at all scales and usually unclear class boundaries. Therefore, dedicated processing schemes are required for the analysis of such data for mineralogical mapping. On the other hand, the variety of optical sensing technology reveals different data attributes and therefore multi-sensor approaches are adapted to solve such complicated mapping problems. In this paper, we devise an adapted multi-optical sensor fusion (MOSFus) workflow which takes the geological characteristics into account. The proposed processing chain exhaustively covers all relevant stages, including data acquisition, preprocessing, feature fusion, and mineralogical mapping. The concept includes (i) a spatial feature extraction based on morphological profiles on RGB data with high spatial resolution, (ii) a specific noise reduction applied on the hyperspectral data that assumes mixed sparse and Gaussian contamination, and (iii) a subsequent dimensionality reduction using a sparse and smooth low rank analysis. The feature extraction approach allows one to fuse heterogeneous data at variable resolutions, scales, and spectral ranges and improve classification substantially. The last step of the approach, an SVM classifier, is robust to unbalanced and sparse training sets and is particularly efficient with complex imaging data. We evaluate the performance of the procedure with two different multi-optical sensor datasets. The results demonstrate the superiority of this dedicated approach over common strategies.

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Mineral Mapping and Vein Detection in Hyperspectral Drill-Core Scans: Application to Porphyry-Type Mineralization

Cited by 30 publications

References 40 publications

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

Multi-Sensor Spectral Imaging of Geological Samples: A Data Fusion Approach Using Spatio-Spectral Feature Extraction

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

Multiple Optical Sensor Fusion for Mineral Mapping of Core Samples

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