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

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.

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

10.5194/egusphere-egu2020-13526 ◽

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

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

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The Rocklea Dome 3D Mineral Mapping Test Data Set

10.5194/essd-2020-336 ◽

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.

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

10.5194/egusphere-egu2020-14952 ◽

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

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. 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.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.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.

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Resolution Enhancement for Drill-Core Hyperspectral Mineral Mapping

Remote Sensing ◽

10.3390/rs13122296 ◽

2021 ◽

Vol 13 (12) ◽

pp. 2296

Author(s):

Isabel Cecilia Contreras Acosta ◽

Mahdi Khodadadzadeh ◽

Richard Gloaguen

Keyword(s):

Mineral Exploration ◽

Resolution Enhancement ◽

Hyperspectral Data ◽

Spatial Sampling ◽

Machine Learning Techniques ◽

Drill Core ◽

Accurate Analysis ◽

Operational Conditions ◽

Mineral Mapping ◽

Supervised Learning Algorithms

Drill-core samples are a key component in mineral exploration campaigns, and their rapid and objective analysis is becoming increasingly important. Hyperspectral imaging of drill-cores is a non-destructive technique that allows for non-invasive and fast mapping of mineral phases and alteration patterns. The use of adapted machine learning techniques such as supervised learning algorithms allows for a robust and accurate analysis of drill-core hyperspectral data. One of the remaining challenge is the spatial sampling of hyperspectral sensors in operational conditions, which does not allow us to render the textural and mineral diversity that is required to map minerals with low abundances and fine structures such as veins and faults. In this work, we propose a methodology in which we implement a resolution enhancement technique, a coupled non-negative matrix factorization, using hyperspectral, RGB images and high-resolution mineralogical data to produce mineral maps at higher spatial resolutions and to improve the mapping of minerals. The results demonstrate that the enhanced maps not only provide better details in the alteration patterns such as veins but also allow for mapping minerals that were previously hidden in the hyperspectral data due to its low spatial sampling.

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The Rocklea Dome 3D Mineral Mapping Test Data Set

Earth System Science Data ◽

10.5194/essd-13-1371-2021 ◽

2021 ◽

Vol 13 (3) ◽

pp. 1371-1383

Author(s):

Carsten Laukamp ◽

Maarten Haest ◽

Thomas Cudahy

Keyword(s):

Remote Sensing ◽

Case Studies ◽

Test Data ◽

Mineral Exploration ◽

Hyperspectral Data ◽

Mineral Deposits ◽

Data Sets ◽

Drill Core ◽

Data Set ◽

Mineral Mapping

Abstract. The integration of surface and subsurface geoscience data is critical for efficient and effective mineral exploration and mining. Publicly accessible data sets 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 (CIDs) 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 bedrock (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 programmes. 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 the quantification of 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 previously 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 white mica and chlorite abundance maps derived from airborne hyperspectral, presented here for the first time, 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.

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MULTI-LABEL CLASSIFICATION FOR DRILL-CORE HYPERSPECTRAL MINERAL MAPPING

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xliii-b3-2020-383-2020 ◽

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.

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Upernavik 98: reconnaissance mineral exploration in North-West Greenland

Geological Survey of Denmark and Greenland Bulletin ◽

10.34194/ggub.v183.5203 ◽

1999 ◽

pp. 39-45 ◽

Cited By ~ 1

Author(s):

Bjørn Thomassen ◽

Johannes Kyed ◽

Agnete Steenfelt ◽

Tapani Tukiainen

Keyword(s):

Mineral Exploration ◽

Mining Industry ◽

Field Work ◽

Geological Survey ◽

Original Series ◽

West Greenland ◽

North West ◽

One Year

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Thomassen, B., Kyed, J., Steenfelt, A., & Tukiainen, T. (1999). Upernavik 98: reconnaissance mineral exploration in North-West Greenland. Geology of Greenland Survey Bulletin, 183, 39-45. https://doi.org/10.34194/ggub.v183.5203 _______________ The Upernavik 98 project is a one-year project aimed at the acquisition of information on mineral occurrences and potential in North-West Greenland between Upernavik and Kap Seddon, i.e. from 72°30′ to 75°30′N (Fig. 1A). A similar project, Karrat 97, was carried out in 1997 in the Uummannaq region 70°30′–72°30′N (Steenfelt et al. 1998a). Both are joint projects between the Geological Survey of Denmark and Greenland (GEUS) and the Bureau of Minerals and Petroleum (BMP), Government of Greenland, and wholly funded by the latter. The main purpose of the projects is to attract the interest of the mining industry. The field work comprised systematic drainage sampling, reconnaissance mineral exploration and spectroradiometric measurements of rock surfaces.

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Qaanaaq 2001: mineral exploration reconnaissance in North-West Greenland

Geological Survey of Denmark and Greenland Bulletin ◽

10.34194/ggub.v191.5141 ◽

2002 ◽

pp. 133-143

Author(s):

Bjørn Thomassen ◽

Peter R. Dawes ◽

Agnete Steenfelt ◽

Johan Ditlev Krebs

Keyword(s):

Mineral Exploration ◽

Mining Industry ◽

Field Work ◽

Original Series ◽

West Greenland ◽

North West ◽

Ice Caps ◽

High Plateau ◽

Set Up ◽

Inland Ice

NOTE: This article was published in a former series of GEUS Bulletin. Please use the original series name when citing this article, for example: Thomassen, B., Dawes, P. R., Steenfelt, A., & Krebs, J. D. (2002). Qaanaaq 2001: mineral exploration reconnaissance in North-West Greenland. Geology of Greenland Survey Bulletin, 191, 133-143. https://doi.org/10.34194/ggub.v191.5141 _______________ Project Qaanaaq 2001, involving one season’s field work, was set up to investigate the mineral occurrences and potential of North-West Greenland between Olrik Fjord and Kap Alexander (77°10´N – 78°10´N; Fig. 1). Organised by the Geological Survey of Denmark and Greenland (GEUS) and the Bureau of Minerals and Petroleum (BMP), Government of Greenland, the project is mainly funded by the latter and has the overall goal of attracting the interest of the mining industry to the region. The investigated region – herein referred to as the Qaanaaq region – comprises 4300 km2 of ice-free land centred on Qaanaaq, the administrative capital of Qaanaap (Thule) municipality. Much of the region is characterised by a 500–800 m high plateau capped by local ice caps and intersected by fjords and glaciers. High dissected terrain occurs in Northumberland Ø and in the hinterland of Prudhoe Land where nunataks are common along the margin of the Inland Ice.

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Current and future limits to automated 3D geological model construction

10.5194/egusphere-egu21-632 ◽

2021 ◽

Author(s):

Mark Jessell

Keyword(s):

Structural Information ◽

Mineral Exploration ◽

Structural Data ◽

Model Construction ◽

Research Centre ◽

Geological Model ◽

Cooperative Research ◽

Geological Modelling ◽

3D Geological Model ◽

Secondary Information

In geological settings characterised by folded and faulted strata, and where good field data exist, we have been able to automate a large part of the 3D modelling process directly from the raw geological database (maps, bedding orientations and drillhole data). The automation is based upon the deconstruction of the geological maps and databases into positional, gradient and spatial and temporal topology information, and the combination of deconstructed data into augmented inputs for 3D geological modelling systems, notably LoopStructural and GemPy.When we try to apply this approach to more complex terranes, such as greenstone belts, we come across two types of problem:<ul><li>1) Insufficient structural data, since the more complexly deformed the geology, the more we need to rely on secondary structural information, such as fold axial traces and vergence to &#8216;solve&#8217; the structures. Unfortunately these types of data are not always stored in national geological databases. One approach to overcoming this is to analyse the simpler (i.e. bedding) data to try and estimate the secondary information automatically.</li> </ul>&#160;<ul><li>2) The available information is unsuited to the logic of the modelling system. Most modern modelling platforms assume the knowledge of a chronostratigraphic hierarchy, however, especially in more complexly deformed regions, a lithostratigraphy may be all that is available. Again a pre-processing of the map and stratigraphic information may be possible to overcome this problem.</li> </ul>This presentation will highlight the progress that has been made, as well as the road-blocks to universal automated 3D geological model construction.&#160;We acknowledge the support of the MinEx CRC and the Loop: Enabling Stochastic 3D Geological Modelling (LP170100985) consortia. The work has been supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government's Cooperative Research Centre Programme. This is MinEx CRC Document 2020/xxx.&#160;

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A new mining life for non-feasible mineral deposits?

10.5194/egusphere-egu21-12115 ◽

2021 ◽

Author(s):

Balazs Bodo ◽

Luis Lopes ◽

Claudio Rossi ◽

Giorgia Stasi ◽

Christian Burlet ◽

...

Keyword(s):

Raw Materials ◽

Mineral Exploration ◽

Mining Industry ◽

Innovative Approach ◽

Mineral Deposits ◽

Biological Inspiration ◽

Social License To Operate ◽

Social License ◽

New Vision ◽

And Control

ROBOMINERS is developing an innovative approach for the exploitation of currently non-feasible mineral deposits. The approach entails the use of a robot-miner - a bio-inspired reconfigurable robot with a modular nature - in a new mining setting where the activities are nearly invisible and where mining presents less socio-environmental constraints, thus contributing to a more safe and sustainable supply of mineral raw materials.The main aim is to design and develop a robotic prototype that is able to perform mining related tasks in settings including both abandoned, currently flooded mines not accessible anymore for conventional mining techniques; or places that have formerly been explored, but whose exploitation was considered as uneconomic due to the small-size of deposits, or their difficulty to access.ROBOMINERS&#8217; innovative approach combines the creation of a new mining ecosystem with novel ideas from other sectors, particularly robotics. At this point, work has been done to understand the best methods for the robotminer&#8217;s development in 1) biological inspiration, 2) perception and localisation tools, 3) behaviour, navigation and control, 4) actuation methods, 5) modularity, 6)autonomy and resilience, and 7) the selective mining ability. All these aspects combined aim to provide the robotminer XXI Century tools for mineral exploration and exploitation of (currently) unfeasible deposits.At the same time, for the vision of a new vision of a mining ecosystem, work is involving studies on 1) developing computer models and simulations, 2) data management and visualisation, 3) rock-mechanical and geotechnical characterisation studies, 4) analysing ground/rock support methods, bulk transportation methods, backfilling types and methods, and 5) sketching relevant upstream and downstream mining industry analogues for the ROBOMINERS concept.&#160;&#160;After design and development, based on the previously mentioned studies, the robot-miner is set to be tested at targeted areas representatives which include abandoned and/or operating mines, small but high-grade mineral deposits, unexplored/explored non-economic occurrences and ultra depth, not&#160; easily accessible environments. Possible candidates for testing purposes include mines in the regions of Cornwall (UK), mines in the Kupferschiefer Formation (e.g. Poland) or coal mines in Belgium.When compared to usual mining methods the ROBOMINERS approach shows: 1) no presence of people in the mine, 2) less mining waste produced, 3) less mining infrastructure, 4) less investment, 5) possibility to explore currently uneconomic resources and 6) new underground small-sized mines, practically &#8220;invisible&#8221;. Altogether, ROBOMINERS can contribute to solve some of the main issues that make mining&#8217;s social license to operate so difficult to get in Europe: land-use, environmental limitations, and socio-economic aspects.

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