ORCID Profile
0000-0002-0987-2206
Current Organisation
CSIRO Australian Resources Research Centre
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Archaeological Science | Inorganic Geochemistry | Geochemistry | Isotope Geochemistry
Expanding Knowledge in the Earth Sciences | Understanding Australia's Past | Mineral Exploration not elsewhere classified |
Publisher: MDPI AG
Date: 10-12-2021
DOI: 10.20944/PREPRINTS202112.0184.V1
Abstract: The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400-25000nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict 8 geochemical parameters. Approximately 175000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The s les and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150000 s les) were used for testing of the method. The data are distributed over 2 large spatial extents (980 km2 and 3025 km2 respectively) and allowed the proposed method to be tested against s les that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150000 validation s les are Fe(0.95/3.01), SiO2(0.96/3.77), Al2O3(0.92/1.27), TiO(0.68/0.13), CaO(0.89/0.41), MgO(0.87/0.35), K2O(0.65/0.21) and LOI(0.90/1.14), given as Parameter(R2/RMSE), and demonstrate that the proposed method is capable of predicting the 8 parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.
Publisher: MDPI AG
Date: 21-12-2021
DOI: 10.20944/PREPRINTS202112.0184.V2
Abstract: The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400-25000nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict 8 geochemical parameters. Approximately 175000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The s les and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150000 s les) were used for testing of the method. The data are distributed over 2 large spatial extents (980 km2 and 3025 km2 respectively) and allowed the proposed method to be tested against s les that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150000 validation s les are Fe(0.95/3.01), SiO2(0.96/3.77), Al2O3(0.92/1.27), TiO(0.68/0.13), CaO(0.89/0.41), MgO(0.87/0.35), K2O(0.65/0.21) and LOI(0.90/1.14), given as Parameter(R2/RMSE), and demonstrate that the proposed method is capable of predicting the 8 parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.
Publisher: Copernicus GmbH
Date: 25-11-2020
Abstract: 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 (Lauk , 2020 0.25919/5ed83bf55be6a) 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 s les. 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.
Publisher: MDPI AG
Date: 31-01-2022
DOI: 10.3390/APP12031547
Abstract: There are two major types of iron ore deposits in the Pilbara Province of Western Australia—banded iron formation (BIF)-hosted iron ore deposits and bedded iron deposits (BID), respectively, named martite–goethite and martite–microplaty hematite and the channel iron deposits (CID). These deposits consist mainly of iron oxides such as magnetite, hematite and goethite the latter have been sub ided into vitreous and ochreous goethite. Combining spectral scanning of diamond drill core, drill chips and pulps collected from these deposits provides a rapid and relatively inexpensive means of assessing the potential mineral makeup within a deposit to make informed qualitative decisions. Additionally, the full width half maximum (FWHM) of the 900 nm 6A1à4T1 crystal field absorption feature within the goethite-dominated region is shown to be related to the type of goethite, namely ochreous and vitreous. The assessment capabilities of the combined metrics are presented in a visual format named as the iron boomerang because of its distinctive manifold. This provides the identification of at least two spectral endmembers comprised of hematite and vitreous goethite, the identification of s les that are moving from a pure hematite to mixed hematite/goethite and lastly into a goethite-dominant-driven regime.
Publisher: Copernicus GmbH
Date: 30-03-2021
DOI: 10.5194/ESSD-13-1371-2021
Abstract: 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 (Lauk , 2020 0.25919/5ed83bf55be6a) 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 s les. 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.
Publisher: Elsevier BV
Date: 2022
Publisher: MDPI AG
Date: 28-12-2021
DOI: 10.3390/APP12010282
Abstract: The efficacy of predicting geochemical parameters with a 2-chain workflow using spectral data as the initial input is evaluated. Spectral measurements spanning the approximate 400–25000 nm spectral range are used to train a workflow consisting of a non-negative matrix function (NMF) step, for data reduction, and a random forest regression (RFR) to predict eight geochemical parameters. Approximately 175,000 spectra with their corresponding chemical analysis were available for training, testing and validation purposes. The s les and their spectral and chemical parameters represent 9399 drillcore. Of those, approximately 20,000 spectra and their accompanying analysis were used for training and 5000 for model validation. The remaining pairwise data (150,000 s les) were used for testing of the method. The data are distributed over two large spatial extents (980 km2 and 3025 km2, respectively) and allowed the proposed method to be tested against s les that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150,000 validation s les are Fe (0.95/3.01), SiO2 (0.96/3.77), Al2O3 (0.92/1.27), TiO (0.68/0.13), CaO (0.89/0.41), MgO (0.87/0.35), K2O (0.65/0.21) and LOI (0.90/1.14), given as Parameter (R2/RMSE), and demonstrate that the proposed method is capable of predicting the eight parameters and is stable enough, in the environment tested, to extend beyond the training sets initial spatial location.
Publisher: MDPI AG
Date: 26-03-2021
DOI: 10.3390/MIN11040347
Abstract: Reflectance spectroscopy allows cost-effective and rapid mineral characterisation, addressing mineral exploration and mining challenges. Shortwave (SWIR), mid (MIR) and thermal (TIR) infrared reflectance spectra are collected in a wide range of environments and scales, with instrumentation ranging from spaceborne, airborne, field and drill core sensors to IR microscopy. However, interpretation of reflectance spectra is, due to the abundance of potential vibrational modes in mineral assemblages, non-trivial and requires a thorough understanding of the potential factors contributing to the reflectance spectra. In order to close the gap between understanding mineral-diagnostic absorption features and efficient interpretation of reflectance spectra, an up-to-date overview of major vibrational modes of rock-forming minerals in the SWIR, MIR and TIR is provided. A series of scripts are proposed that allow the extraction of the relative intensity or wavelength position of single absorption and other mineral-diagnostic features. Binary discrimination diagrams can assist in rapidly evaluating mineral assemblages, and relative abundance and chemical composition of key vector minerals, in hydrothermal ore deposits. The aim of this contribution is to make geologically relevant information more easily extractable from reflectance spectra, enabling the mineral resources and geoscience communities to realise the full potential of hyperspectral sensing technologies.
Publisher: MDPI AG
Date: 29-01-2027
DOI: 10.3390/MIN11020136
Abstract: A workflow incorporating hyperspectral reflectance data, hull corrections, absorption feature extraction and clustering is presented. The workflow is applied to dense hyperspectral datasets, as collected by hyperspectral drill core logging systems. The extracted absorption features of the reflectance spectra collected from drill cores are shown to form assemblage clusters when plotting the wavelength position of the first, second and third deepest absorption features in two and three dimensions. Using an unsupervised clustering method to establish clusters based on the extracted absorption features yields viewable down hole distributions of similar mineral assemblages. The proposed workflow has the potential for the rapid identification of differing lithologies, alteration and/or weathering overprints. Application of the workflow with no a-priori assumptions about the composition of the potential mineral assemblages provides a means of generating an informative overview of the dataset that is not biased or constrained by preconceptions. The workflow can easily be added to the current workflows of geologists whom are working with dense hyperspectral data to provide an overview of the potential down hole mineral assemblages and aid in the visual logging process or assist in quickly identifying areas for more detailed observation. Furthermore, key mineralogical parameters for resource characterisation, such as the presence of clay minerals can be assessed in a cost and time efficient manner. The proposed workflow is applied to spectra collected from four different drill cores collected in the Gawler Craton located in South Australia and demonstrates the potential outlined above.
Publisher: Springer Science and Business Media LLC
Date: 20-03-2021
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-10474
Abstract: & & Earth observation is invaluable for the agricultural sector as well as the critical metals sector, providing cost-effective, spatially comprehensive information about Earth& #8217 s surface composition from the regional to paddock/mine-scale. A wide range of remote sensing instruments are used to monitor soils, to give information on properties such as moisture and mineralogy. At the same time, remote sensing data facilitate the discovery and mining of mineral deposits, including iron ore, copper and other metals critical for the transition of the fossil fuel-based energy sector to a sustainable, renewable energy future. One common factor of these two sectors is that all Earth observation systems require calibration sites that help to ensure the data being collected is of high accuracy. Another common factor is that both sectors require ground validation of the remotely sensed data, producing a plethora of publicly available Earth surface data distributed across numerous web portals and platforms. Both sectors aim, ultimately, towards characterising the composition of the subsurface - which starts in both sectors at Earth& #8217 s surface and reaches to 10s or even 100s of metres below. This can be achieved by developing conceptual models that describe the weathering of bedrock in the soil/regolith. In mineral resource exploration, specific weathering-resistant minerals (e.g. talc) can be traced at Earth& #8217 s surface by means of Earth observation to characterise the type of bedrock through cover (i.e. beneath the soil/regolith). Another ex le is the mapping of differences in kaolin crystallinity at Earth& #8217 s surface and in the subsurface (e.g. drilling, trenches) to infer the distribution of in-situ versus transported regolith, which is of key importance for raw materials exploration. Remote sensing is also commonly used for collecting baseline environmental data prior to mining and for monitoring its impact on the environment during and after the process. In soil science, infrared spectral measurements have been conducted on soil s les in laboratories for estimation of soil properties, such as soil carbon, pH, EC. These estimations require a training library as well as standardised preparation of the s les and measurement technique. The ultimate goal is the accurate measurement of these soil properties using remote sensing, where complex variance of the nature of the materials and illumination conditions exists. & & & & & This paper discusses opportunities for sharing facilities, data, workflows and methods for collecting, processing and interpreting remote and proximal multi- and hyperspectral sensing technologies. For this, publicly available mineralogical and geochemical data sets collected from the critical zone, such as in the frame of the National Geochemical Survey of Australia (NGSA www.ga.gov.au/about rojects/resources/national-geochemical-survey) project and AuScope& #8217 s National Virtual Core Library Infrastructure Program (NVCL www.auscope.org.au/nvcl), as well as publicly available Earth observation products, such as the Australian ASTER Geoscience Products, will be used to demonstrate the multidisciplinary applications of multi- and hyperspectral remote and proximal sensing data. For the benefit of meeting the United Nations& #8217 Sustainable Development Goals, agriculture, resources and environment sectors should overcome unnecessary competition and work hand in hand.& &
Start Date: 07-2022
End Date: 06-2024
Amount: $344,864.00
Funder: Australian Research Council
View Funded Activity