Andrew Rodger

The so-called iron boomerang is constructed by using a combination of spectral metrics derived from the 900 nm ⁶A₁→⁴T₁ Fe³⁺ crystal field absorption. Previous work showed the iron boomerang provides qualitative information about the mineralogical type of spectral datasets of Western Australian iron ore deposits. That work is expanded to demonstrate how the shape of the iron boomerang is driven by a linear mixing regime between hematite and ochreous and vitreous goethite. The iron boomerang enveloping shape is defined by mixing pathways between 3 different 2-endmember systems of hematite–ochreous goethite, hematite–vitreous goethite and ochreous–vitreous goethite and a 3-endmember mixing regime in the interior of the boomerang. This provides a novel methodology of quantifying the relative hematite, ochreous, and vitreous goethite content from spectrally derived data. A combination of 4 spectral metrics as input features to a random forest regression model results in modelling root mean squared errors (RMSE) of approximately 4% when all endmembers are known and in a system where the endmembers are not known the RMSE is approximately 10%. Additionally, a means of identifying potential hematite endmembers from a spectral dataset is presented. Lastly, the regression models are applied to 3 iron ore diamond drillcore from the Hamersley Province in Western Australia and demonstrates that the proportions of hematite, ochreous, and vitreous goethite can be estimated downhole which, in turn, provides ore zone delineation and hardcap and hydrated zone identification.

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 subdivided 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 samples that are moving from a pure hematite to mixed hematite/goethite and lastly into a goethite-dominant-driven regime.

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 samples 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 samples) 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 samples that are spatially distant from the initial training points. Global R² scores and wt.% RMSE on the 150,000 validation samples 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.

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 samples 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 samples) 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 samples that are spatially distant from the initial training points. Global R2 scores and wt.% RMSE on the 150000 validation samples 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.

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.

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.

An increasingly common requirement in remote sensing is the integration of hyperspectral data collected simultaneously from different sensors (and fore-optics) operating across different wavelength ranges. Data from one module are often relied on to correct information in the other, such as aerosol optical thickness (AOT) and columnar water vapor (CWV). This paper describes problems associated with this process and recommends an improved strategy for processing remote sensing data, collected from both visible to near-infrared and shortwave infrared modules, to retrieve accurate AOT, CWV, and surface reflectance values. This strategy includes a workflow for radiometric and spatial cross-calibration and a method to retrieve atmospheric parameters and surface reflectance based on a radiative transfer function. This method was tested using data collected with the Compact Airborne Spectrographic Imager (CASI) and SWIR Airborne Spectrographic Imager (SASI) from a site in Huailai County, Hebei Province, China. Various methods for retrieving AOT and CWV specific to this region were assessed. The results showed that retrieving AOT from the remote sensing data required establishing empirical relationships between 465.6 nm/659 nm and 2105 nm, augmented by ground-based reflectance validation data, and minimizing the merit function based on AOT@550 nm optimization. The paper also extends the second-order difference algorithm (SODA) method using Powell’s methods to optimize CWV retrieval. The resulting CWV image has fewer residual surface features compared with the standard methods. The derived remote sensing surface reflectance correlated significantly with the ground spectra of comparable vegetation, cement road and soil targets. Therefore, the method proposed in this paper is reliable enough for integrated atmospheric correction and surface reflectance retrieval from hyperspectral remote sensing data. This study provides a good reference for surface reflectance inversion that lacks synchronized atmospheric parameters.