3D modelling of a mineral deposit using drill core hyperspectral data

2020 ◽  
Author(s):  
Roberto De La Rosa ◽  
Mahdi Khodadadzadeh ◽  
Cecilia Contreras ◽  
Laura Tusa ◽  
Moritz Kirsch ◽  
...  
Keyword(s):  
Categorical Data ◽  
Near Infrared ◽  
Mining Industry ◽  
Structural Features ◽  
Derived Categories ◽  
Hyperspectral Data ◽  
Drill Core ◽  
Data Set ◽  
Core Samples ◽  
Core Logging

<p><span>Drill core samples have been traditionally used by the mining industry to make resource estimations and to build geological models. The hyperspectral drill core scanning has become a popular tool in mineral exploration because it provides a non-destructive method to rapidly characterise structural features, alteration patterns and rock mineralogy in a cost effective way. </span></p><p><span>Typically, the hyperspectral sensors cover a wide spectral range from visible and near-infrared (VNIR) to short and long wave infrared (SWIR and LWIR). The spectral features in this range will help to characterize a large number of mineral phases and complement the traditional core logging techniques. The hyperspectral core scanning provide mineralogical information in a millimetre scale for the entire borehole, which fills the gap between the microscopic scale of some of the laboratory analytical methods or the sparse chemical assays and the meter scale from the lithological descriptions.</span></p><p><span>However, applying this technique to the core samples of an entire ore deposit results in big datasets. Therefore, there is the need of a workflow to build a 3D geological model conditioned by the data applying suitable data reduction methods and appropriate interpolation techniques.</span></p><p><span>This contribution presents a case study in the combination of traditional core logging and hyperspectral core logging for geological modelling. To attain mineral and alteration maps from the hyperspectral data, unsupervised classification techniques were applied generating a categorical data set. The amount of data was reduced by the application of a domain generation algorithm based on the hyperspectral information. The domain generated by the algorithm is a compositional categorical data set that was then fed to condition the application of stochastic Plurigaussian simulations in the construction of 3D models of geological domains. This technique allows to simulate the spatial distribution of the hyperspectral derived categories, to make a resource estimation and to calculate its associated uncertainty.</span></p>

A supervised technique for drill-core mineral mapping using Hyperspectral data

2020 ◽  
Author(s):  
Cecilia Contreras ◽  
Mahdi Khodadadzadeh ◽  
Laura Tusa ◽  
Richard Gloaguen
Keyword(s):  
Machine Learning ◽  
Near Infrared ◽  
Hyperspectral Data ◽  
Supervised Machine Learning ◽  
Machine Learning Techniques ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Core Logging ◽  
Learning Techniques

<p>Drilling is a key task in exploration campaigns to characterize mineral deposits at depth. Drillcores<br>are first logged in the field by a geologist and with regards to, e.g., mineral assemblages,<br>alteration patterns, and structural features. The core-logging information is then used to<br>locate and target the important ore accumulations and select representative samples that are<br>further analyzed by laboratory measurements (e.g., Scanning Electron Microscopy (SEM), Xray<br>diffraction (XRD), X-ray Fluorescence (XRF)). However, core-logging is a laborious task and<br>subject to the expertise of the geologist.<br>Hyperspectral imaging is a non-invasive and non-destructive technique that is increasingly<br>being used to support the geologist in the analysis of drill-core samples. Nonetheless, the<br>benefit and impact of using hyperspectral data depend on the applied methods. With this in<br>mind, machine learning techniques, which have been applied in different research fields,<br>provide useful tools for an advance and more automatic analysis of the data. Lately, machine<br>learning frameworks are also being implemented for mapping minerals in drill-core<br>hyperspectral data.<br>In this context, this work follows an approach to map minerals on drill-core hyperspectral data<br>using supervised machine learning techniques, in which SEM data, integrated with the mineral<br>liberation analysis (MLA) software, are used in training a classifier. More specifically, the highresolution<br>mineralogical data obtained by SEM-MLA analysis is resampled and co-registered<br>to the hyperspectral data to generate a training set. Due to the large difference in spatial<br>resolution between the SEM-MLA and hyperspectral images, a pre-labeling strategy is<br>required to link these two images at the hyperspectral data spatial resolution. In this study,<br>we use the SEM-MLA image to compute the abundances of minerals for each hyperspectral<br>pixel in the corresponding SEM-MLA region. We then use the abundances as features in a<br>clustering procedure to generate the training labels. In the final step, the generated training<br>set is fed into a supervised classification technique for the mineral mapping over a large area<br>of a drill-core. The experiments are carried out on a visible to near-infrared (VNIR) and shortwave<br>infrared (SWIR) hyperspectral data set and based on preliminary tests the mineral<br>mapping task improves significantly.</p>

scholarly journals MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

2020 ◽  
Vol XLIII-B3-2020 ◽  
pp. 383-388
Author(s):  
I. C. Contreras ◽  
M. Khodadadzadeh ◽  
R. Gloaguen
Keyword(s):  
Near Infrared ◽  
Short Wave ◽  
Hyperspectral Data ◽  
Training Data ◽  
Electromagnetic Spectrum ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Mapping Task ◽  
Liberation Analysis

Abstract. A multi-label classification concept is introduced for the mineral mapping task in drill-core hyperspectral data analysis. As opposed to traditional classification methods, this approach has the advantage of considering the different mineral mixtures present in each pixel. For the multi-label classification, the well-known Classifier Chain method (CC) is implemented using the Random Forest (RF) algorithm as the base classifier. High-resolution mineralogical data obtained from Scanning Electron Microscopy (SEM) instrument equipped with the Mineral Liberation Analysis (MLA) software are used for generating the training data set. The drill-core hyperspectral data used in this paper cover the visible-near infrared (VNIR) and the short-wave infrared (SWIR) range of the electromagnetic spectrum. The quantitative and qualitative analysis of the obtained results shows that the multi-label classification approach provides meaningful and descriptive mineral maps and outperforms the single-label RF classification for the mineral mapping task.

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

Minerals ◽  
2019 ◽  
Vol 9 (2) ◽  
pp. 122 ◽  
Author(s):  
Laura Tusa ◽  
Louis Andreani ◽  
Mahdi Khodadadzadeh ◽  
Cecilia Contreras ◽  
Paul Ivascanu ◽  
...  
Keyword(s):  
Mineral Exploration ◽  
Mining Industry ◽  
Structural Data ◽  
Short Wave ◽  
Variable Density ◽  
Drill Core ◽  
Distribution Maps ◽  
Mineral Mapping ◽  
Core Logging ◽  
Core Mapping

The rapid mapping and characterization of specific porphyry vein types in geological samples represent a challenge for the mineral exploration and mining industry. In this paper, a methodology to integrate mineralogical and structural data extracted from hyperspectral drill-core scans is proposed. The workflow allows for the identification of vein types based on minerals having significant absorption features in the short-wave infrared. The method not only targets alteration halos of known compositions but also allows for the identification of any vein-like structure. The results consist of vein distribution maps, quantified vein abundances, and their azimuths. Three drill-cores from the Bolcana porphyry system hosting veins of variable density, composition, orientation, and thickness are analysed for this purpose. The results are validated using high-resolution scanning electron microscopy-based mineral mapping techniques. We demonstrate that the use of hyperspectral scanning allows for faster, non-invasive and more efficient drill-core mapping, providing a useful tool for complementing core-logging performed by on-site geologists.

A novel approach for the geospatial modelling and resource assessment of tailings storage facilities

2021 ◽  
Author(s):  
Rosie Blannin ◽  
Max Frenzel ◽  
Jens Gutzmer
Keyword(s):  
Mine Drainage ◽  
Drill Core ◽  
Data Set ◽  
Heterogeneous Distribution ◽  
Core Samples ◽  
Surface Samples ◽  
Novel Approach ◽  
Geospatial Modelling ◽  
Log Ratio ◽  
Storage Facilities

<p>Tailings are the fine-grained residues of ore processing operations, typically stored in dedicated tailings storage facilities (TSFs). Despite being viewed as ‘waste’ materials, tailings can contain significant amounts of valuable metals which were not recovered by original processing techniques or were previously not of economic interest. Re-processing of tailings deposits for the recovery of remaining metals has the additional benefits of mitigation of environmental hazards posed by the TSFs, such as Acid Mine Drainage (AMD). The estimation of mineral resources requires the construction of accurate and reproducible geospatial models. However, the sedimentary-style deposition and subsequent weathering of tailings results in a complex internal structure which is challenging to model, with a laterally and vertically heterogeneous distribution of the minerals comprising the residues. The present study investigates a novel approach for the geospatial modelling of a TSF case study. The surface of the tailings deposit was densely sampled in order to assess the intrinsic horizontal variability. Drill core samples were taken from a depth of 1-3 m, on a 30 m grid and nested grids of 15 m and 7.5 m, with additional random and twin holes. The entire depth of the TSF was sampled in 2 m intervals with a total of 10 drill holes to assess vertical variability. All drill core samples were analysed with x-ray fluorescence spectrometry and inductively coupled plasma mass spectrometry. The compositional data was log-ratio-transformed and variography and subsequent ordinary kriging and co-kriging were performed on the surface samples. The variogram models obtained for the surface samples were then applied for kriging of the deeper layers. Historical photographs of the surface of the TSF were used to improve estimates with co-kriging for the corresponding layers. The entire data set will be used to determine the most efficient sampling approach for the resource estimation of TSFs.</p>

scholarly journals Mineral mapping by hyperspectral remote sensing in West Greenland using airborne, ship-based and terrestrial platforms

2018 ◽  
pp. 47-50 ◽  
Author(s):  
Sara Salehi ◽  
Simon Mose Thaarup
Keyword(s):  
Near Infrared ◽  
Hyperspectral Data ◽  
Geological Mapping ◽  
Infrared Range ◽  
Remote Measurement ◽  
Electromagnetic Spectrum ◽  
Similar Data ◽  
Data Set ◽  
Specific Chemical ◽  
West Greenland

While multispectral images have been in regular use since the 1970s, the widespread use of hyperspectral images is a relatively recent trend. This technology comprises remote measurement of specific chemical and physical properties of surface materials through imaging spectroscopy. Regional geological mapping and mineral exploration are among the main applications that may benefit from hyperspectral technology. Minerals and rocks exhibit diagnostic spectral features throughout the electromagnetic spectrum that allow their chemical composition and relative abundance to be mapped. Most studies using hyperspectral data for geological applications have concerned areas with arid to semi-arid climates, and using airborne data collection. Other studies have investigated terrestrial outcrop sensing and integration with laser scanning 3D models in ranges of up to a few hundred metres, whereas less attention has been paid to ground-based imaging of more distant targets such as mountain ridges, cliffs or the walls of large pits. Here we investigate the potential of using such data in well-exposed Arctic regions with steep topography as part of regional geological mapping field campaigns, and to test how airborne hyperspectral data can be combined with similar data collected on the ground or from moving platforms such as a small ship. The region between the fjords Ikertoq and Kangerlussuaq (Søndre Strømfjord) in West Greenland was selected for a field study in the summer of 2016. This region is located in the southern part of the Palaeoproterozoic Nagssugtoqidian orogen and consists of high-grade metamorphic ortho- and paragneisses and metabasic rocks (see below). A regional airborne hyperspectral data set (i.e. HyMAP) was acquired here in 2002 (Tukiainen & Thorning 2005), comprising 54 flight lines covering an area of c. 7500 km2; 19 of these flight lines were selected for the present study (Fig. 1). The target areas visited in the field were selected on the basis of preliminary interpretations of HyMap scenes and geology (Korstgård 1979). Two different sensors were utilised to acquire the new hyperspectral data, predominantly a Specim AisaFenix hyperspectral scanner due to its wide spectral range covering the visible to near infrared and shortwave infrared parts of the electromagnetic spectrum. A Rikola Hyperspectral Imager constituted a secondary imaging system. It is much smaller and lighter than the Fenix scanner, but is spectrally limited to the visible near infrared range. The results obtained from combining the airborne hyperspectral data and the Rikola instrument are presented in Salehi (2018), this volume. In addition, representative samples of the main rock types were collected for subsequent laboratory analysis. A parallel study was integrated with geological and 3D photogrammetric mapping in Karrat region farther north in West Greenland (Rosa et al. 2017; Fig. 1).

scholarly journals The Rocklea Dome 3D Mineral Mapping Test Data Set

2020 ◽  
Author(s):  
Carsten Laukamp ◽  
Maarten Haest ◽  
Thomas Cudahy
Keyword(s):  
Remote Sensing ◽  
Case Studies ◽  
Test Data ◽  
Mineral Exploration ◽  
Hyperspectral Data ◽  
Mineral Deposits ◽  
Drill Core ◽  
Data Set ◽  
Mineral Mapping ◽  
Barren Ground

Abstract. The integration of surface and subsurface geoscience data is critical for efficient and effective mineral exploration and mining. Publicly accessible datasets to evaluate the various geoscience analytical tools and their effectiveness for characterisation of mineral assemblages and lithologies or discrimination of ore from waste are however scarce. The open access Rocklea Dome 3D Mineral Mapping Test Data Set (Laukamp, 2020; https://doi.org/10.25919/5ed83bf55be6a) provides an opportunity for evaluating proximal and remote sensing data, validated and calibrated by independent geochemical and mineralogical analyses, for exploration of channel-iron deposits (CID) through cover. We present hyperspectral airborne, surface and drill core reflectance spectra collected in the visible-near infrared and shortwave infrared wavelength ranges (VNIR-SWIR; 350 to 2500 nm), as well as whole rock geochemistry obtained by means of X-Ray fluorescence analysis and loss on ignition measurements of drill core samples. The integration of surface with subsurface hyperspectral data collected in the frame of previously published Rocklea Dome 3D Mineral Mapping case studies demonstrated that about 30 % of exploration drill holes were sunk into barren ground and could have been of better use, located elsewhere, if airborne hyperspectral imagery had been consulted for drill hole planning. The remote mapping of transported Tertiary detritals (i.e. potential hosts of channel iron ore resources) versus weathered in situ Archaean geology (i.e. barren ground) has significant implications for other areas where cover (i.e. regolith and/or sediments covering bedrock hosting mineral deposits) hinders mineral exploration. Hyperspectral remote sensing represents a cost-effective method for regolith landform mapping required for planning drilling programs. In the Rocklea Dome area, vegetation unmixing methods applied to airborne hyperspectral data, integrated with subsurface data, resulted in seamless mapping of ore zones from the weathered surface to the base of the CID – a concept that can be applied to other mineral exploration and mineral deposit studies. Furthermore, the associated, independent calibration data allowed to quantify iron oxide phases and associated mineralogy from hyperspectral data. Using the Rocklea Dome data set, novel geostatistical clustering methods were applied to the drill core data sets for ore body domaining that introduced scientific rigour to a traditionally subjective procedure, resulting in reproducible objective domains that are critical for the mining process. Beyond the already published case studies, the Rocklea Dome 3D Mineral Mapping Test Data Set has the potential to develop new methods for advanced resource characterisation and develop new applications that aid exploration for mineral deposits through cover. The here newly presented white mica and chlorite abundance maps derived from airborne hyperspectral highlight the additional applications of remote sensing for geological mapping and could help to evaluate newly launched hyper- and multispectral spaceborne systems for geoscience and mineral exploration.

Automated Core Logging Technology for Geotechnical Assessment: A Study on Core from the Cadia East Porphyry Deposit

2019 ◽  
Vol 114 (8) ◽  
pp. 1495-1511 ◽  
Author(s):  
Cassady L. Harraden ◽  
Matthew J. Cracknell ◽  
James Lett ◽  
Ron F. Berry ◽  
Ronell Carey ◽  
...  
Keyword(s):  
Hyperspectral Data ◽  
Classification Systems ◽  
Drill Core ◽  
Rock Classification ◽  
Porphyry Deposit ◽  
Core Surface ◽  
Geotechnical Assessment ◽  
Core Logging ◽  
Q Index ◽  
Geotechnical Model

Abstract The Cadia East porphyry deposit, located approximately 20 km south of Orange, New South Wales, Australia, contains a significant resource of copper and gold. This resource is hosted within the Forest Reefs Volcanics and is spatially and temporally associated with the Cadia Intrusive Complex. To extract ore, the underground mine currently uses the block cave mining method. The Cadia East geotechnical model provides data inputs into a range of numerical and empirical analysis methods that make up the foundation for mine design. These data provide input into the construction of stress models, caveability models, ground support design, and fragmentation analysis. This geotechnical model encompasses two commonly used rock classification systems that quantify ground conditions: (1) rock mass rating (RMR) and (2) rock tunneling quality index (Q index). The RMR and Q index are calculated from estimates of rock quality designation (RQD), number of fracture sets, fracture roughness, fracture alteration, and fracture spacing. Geologists and geotechnical engineers collect information used to produce these estimates by manually logging sections of drill core, a time-consuming task that can result in inconsistent data. Modern automated core scanning technologies offer opportunities to rapidly collect data from larger samples of drill core. These automated core logging systems generate large volumes of spatially and spectrally consistent data, including a model of the drill core surface from a laser profiling system. Core surface models are used to extract detailed measurements of fracture location, orientation, and roughness from oriented drill core. These data are combined with other morphological and mineralogical outputs from automated hyperspectral core logging systems to estimate RMR and the Q index systematically over contiguous drill core intervals. The goal of this study was to develop a proof-of-concept methodology that extracts geotechnical index parameters from hyperspectral and laser topographic data collected from oriented drill core. Hyperspectral data from the Cadia East mine were used in this case study to assess the methods. The results show that both morphological and mineralogical parameters that contribute to the RMR and Q index can be extracted from the automated core logging data. This approach provides an opportunity to capture consistent geologic, mineralogical, and geotechnical data at a scale that is too time-consuming to achieve via manual data collection.

scholarly journals Preprocessing Ground-Based Visible/Near Infrared Imaging Spectroscopy Data Affected by Smile Effects

Sensors ◽  
2019 ◽  
Vol 19 (7) ◽  
pp. 1543 ◽  
Author(s):  
Henning Buddenbaum ◽  
Michael Watt ◽  
Rebecca Scholten ◽  
Joachim Hill
Keyword(s):  
Pinus Contorta ◽  
Near Infrared ◽  
Infrared Imaging ◽  
Imaging Spectroscopy ◽  
Conveyor Belt ◽  
Hyperspectral Data ◽  
Field Of View ◽  
Data Set ◽  
Potted Plants ◽  
Very High

A data set of very high-resolution visible/near infrared hyperspectral images of young Pinus contorta trees was recorded to study the effects of herbicides on this invasive species. The camera was fixed on a frame while the potted trees were moved underneath on a conveyor belt. To account for changing illumination conditions, a white reference bar was included at the edge of each image line. Conventional preprocessing of the images, i.e., dividing measured values by values from the white reference bar in the same image line, failed and resulted in bad quality spectra with oscillation patterns that are most likely due to wavelength shifts across the sensor’s field of view (smile effect). An additional hyperspectral data set of a Spectralon white reference panel could be used to characterize and correct the oscillations introduced by the division, resulting in a high quality spectra that document the effects of herbicides on the reflectance characteristics of coniferous trees. While the spectra of untreated trees remained constant over time, there were clear temporal changes in the spectra of trees treated with both herbicides. One herbicide worked within days, the other one within weeks. Ground-based imaging spectroscopy with meaningful preprocessing proved to be an appropriate tool for monitoring the effects of herbicides on potted plants.

scholarly journals Automated Acid Rock Drainage Indexing from Drill Core Imagery

Minerals ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 571 ◽  
Author(s):  
Matthew Cracknell ◽  
Anita Parbhakar-Fox ◽  
Laura Jackson ◽  
Ekaterina Savinova
Keyword(s):  
Management Practices ◽  
Near Infrared ◽  
Acid Rock Drainage ◽  
Mining Industry ◽  
Image Resolution ◽  
Drill Core ◽  
Automated Classification ◽  
Accurate Identification ◽  
Acid Rock

The automated classification of acid rock drainage (ARD) potential developed in this study is based on a manual ARD Index (ARDI) logging code. Several components of the ARDI require accurate identification of sulfide minerals that hyperspectral drill core scanning technologies cannot yet report. To overcome this, a new methodology was developed that uses red–green–blue (RGB) true color images generated by Corescan® to determine the presence or absence of sulfides using supervised classification. The output images were then recombined with Corescan® visible to near infrared-shortwave infrared (VNIR-SWIR) mineral classifications to obtain information that allowed an automated ARDI (A-ARDI) assessment to be performed. To test this, A-ARDI estimations and the resulting acid-forming potential classifications for 22 drill core samples obtained from a porphyry Cu–Au deposit were compared to ARDI classifications made from manual observations and geochemical and mineralogical analyses. Results indicated overall agreement between automated and manual ARD potential classifications and those from geochemical and mineralogical analyses. Major differences between manual and automated ARDI results were a function of differences in estimates of sulfide and neutralizer mineral concentrations, likely due to the subjective nature of manual estimates of mineral content and automated classification image resolution limitations. The automated approach presented here for the classification of ARD potential offers rapid and repeatable outcomes that complement manual and analyses derived classifications. Methods for automated ARD classification from digital drill core data represent a step-change for geoenvironmental management practices in the mining industry.

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