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Using machine learning to map Western Australian landscapes for mineral exploration / Thomas Albrecht in ISPRS International journal of geo-information, vol 10 n° 7 (July 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 7 (July 2021) . - n° 459 Titre : Using machine learning to map Western Australian landscapes for mineral exploration Type de document : Article/Communication Auteurs : Thomas Albrecht, Auteur ; Ignacio Gonzalez-Alvarez, Auteur ; Jens Klump, Auteur Année de publication : 2021 Article en page(s) : n° 459 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] Australie occidentale (Australie) [Termes IGN] cartographie automatique [Termes IGN] classification dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] géomorphologie [Termes IGN] modèle numérique de surface [Termes IGN] prospection minérale [Termes IGN] Python (langage de programmation) Résumé : (auteur) Landscapes evolve due to climatic conditions, tectonic activity, geological features, biological activity, and sedimentary dynamics. Geological processes at depth ultimately control and are linked to the resulting surface features. Large regions in Australia, West Africa, India, and China are blanketed by cover (intensely weathered surface material and/or later sediment deposition, both up to hundreds of metres thick). Mineral exploration through cover poses a significant technological challenge worldwide. Classifying and understanding landscape types and their variability is of key importance for mineral exploration in covered regions. Landscape variability expresses how near-surface geochemistry is linked to underlying lithologies. Therefore, landscape variability mapping should inform surface geochemical sampling strategies for mineral exploration. Advances in satellite imaging and computing power have enabled the creation of large geospatial data sets, the sheer size of which necessitates automated processing. In this study, we describe a methodology to enable the automated mapping of landscape pattern domains using machine learning (ML) algorithms. From a freely available digital elevation model, derived data, and sample landclass boundaries provided by domain experts, our algorithm produces a dense map of the model region in Western Australia. Both random forest and support vector machine classification achieve approximately 98% classification accuracy with a reasonable runtime of 48 minutes on a single Intel® Core™ i7-8550U CPU core. We discuss computational resources and study the effect of grid resolution. Larger tiles result in a more contiguous map, whereas smaller tiles result in a more detailed and, at some point, noisy map. Diversity and distribution of landscapes mapped in this study support previous results. In addition, our results are consistent with the geological trends and main basement features in the region. Mapping landscape variability at a large scale can be used globally as a fundamental tool for guiding more efficient mineral exploration programs in regions under cover. Numéro de notice : A2021-546 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10070459 Date de publication en ligne : 06/07/2021 En ligne : https://doi.org/10.3390/ijgi10070459 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98048 [article]

Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data / Ehsan Farahbakhsh in International Journal of Remote Sensing IJRS, vol 41 n°5 (01 - 08 février 2020)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 41 n°5 (01 - 08 février 2020) . - pp 1760 - 1787 Titre : Computer vision-based framework for extracting tectonic lineaments from optical remote sensing data Type de document : Article/Communication Auteurs : Ehsan Farahbakhsh, Auteur ; Rohitash Chandra, Auteur ; Hugo K. H. Olierook, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1760 - 1787 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Australie occidentale (Australie) [Termes IGN] cartographie géologique [Termes IGN] détection de contours [Termes IGN] digue [Termes IGN] faille géologique [Termes IGN] filtre [Termes IGN] image Landsat-8 [Termes IGN] linéament [Termes IGN] tectonique [Termes IGN] vision par ordinateur Résumé : (auteur) The extraction of tectonic lineaments from digital satellite data is a fundamental application in remote sensing. The location of tectonic lineaments such as faults and dykes are of interest for a range of applications, particularly because of their association with hydrothermal mineralization. Although a wide range of applications have utilized computer vision techniques, a standard workflow for application of these techniques to tectonic lineament extraction is lacking. We present a framework for extracting tectonic lineaments using computer vision techniques. The proposed framework is a combination of edge detection and line extraction algorithms for extracting tectonic lineaments using optical remote sensing data. It features ancillary computer vision techniques for reducing data dimensionality, removing noise and enhancing the expression of lineaments. The efficiency of two convolutional filters are compared in terms of enhancing the lineaments. We test the proposed framework on Landsat 8 data of a mineral-rich portion of the Gascoyne Province in Western Australia. To validate the results, the extracted lineaments are compared to geologically mapped structures by the Geological Survey of Western Australia (GSWA). The results show that the best correlation between our extracted tectonic lineaments and the GSWA tectonic lineament map is achieved by applying a minimum noise fraction transformation and a Laplacian filter. Application of a directional filter shows a strong correlation with known sites of hydrothermal mineralization. Hence, our method using either filter can be used for mineral prospectivity mapping in other regions where faults are exposed and observable in optical remote sensing data. Numéro de notice : A2020-464 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/01431161.2019.1674462 Date de publication en ligne : 11/10/2019 En ligne : https://doi.org/10.1080/01431161.2019.1674462 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94902 [article]

Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) / R. Murphy in ISPRS Journal of photogrammetry and remote sensing, vol 75 (January 2013)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 75 (January 2013) . - pp 29 - 39 Titre : Mapping the distribution of ferric iron minerals on a vertical mine face using derivative analysis of hyperspectral imagery (430–970 nm) Type de document : Article/Communication Auteurs : R. Murphy, Auteur ; S. Monteiro, Auteur Année de publication : 2013 Article en page(s) : pp 29 - 39 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Advanced Visible & Near Infrared Radiometer [Termes IGN] analyse d'image numérique [Termes IGN] Australie occidentale (Australie) [Termes IGN] dérivée [Termes IGN] image hyperspectrale [Termes IGN] mine de fer [Termes IGN] photographie infrarouge couleur [Termes IGN] rayonnement proche infrarouge [Termes IGN] réflectance spectrale Résumé : (Auteur) Hyperspectral imagery is used to map the distribution of iron and separate iron ore from shale (a waste product) on a vertical mine face in an open-pit mine in the Pilbara, Western Australia. Vertical mine faces have complex surface geometries which cause large spatial variations in the amount of incident and reflected light. Methods used to analyse imagery must minimise these effects whilst preserving any spectral variations between rock types and minerals. Derivative analysis of spectra to the 1st-, 2nd- and 4th-order is used to do this. To quantify the relative amounts and distribution of iron, the derivative spectrum is integrated across the visible and near infrared spectral range (430–970 nm) and over those wavelength regions containing individual peaks and troughs associated with specific iron absorption features. As a test of this methodology, results from laboratory spectra acquired from representative rock samples were compared with total amounts of iron minerals from X-ray diffraction (XRD) analysis. Relationships between derivatives integrated over the visible near-infrared range and total amounts (% weight) of iron minerals were strongest for the 4th- and 2nd-derivative (R2 = 0.77 and 0.74, respectively) and weakest for the 1st-derivative (R2 = 0.56). Integrated values of individual peaks and troughs showed moderate to strong relationships in 2nd- (R2 = 0.68–0.78) and 4th-derivative (R2 = 0.49–0.78) spectra. The weakest relationships were found for peaks or troughs towards longer wavelengths. The same derivative methods were then applied to imagery to quantify relative amounts of iron minerals on a mine face. Before analyses, predictions were made about the relative abundances of iron in the different geological zones on the mine face, as mapped from field surveys. Integration of the whole spectral curve (430–970 nm) from the 2nd- and 4th-derivative gave results which were entirely consistent with predictions. Conversely, integration of the 1st-derivative gave results that did not fit with predictions nor distinguish between zones with very large and small amounts of iron oxide. Classified maps of ore and shale were created using a simple level-slice of the 1st-derivative reflectance at 702, 765 and 809 nm. Pixels classified as shale showed a similar distribution to kaolinite (an indicator of shales in the region), as mapped by the depth of the diagnostic kaolinite absorption feature at 2196 nm. Standard statistical measures of classification performance (accuracy, precision, recall and the Kappa coefficient of agreement) indicated that nearly all of the pixels were classified correctly using 1st-derivative reflectance at 765 and 809 nm. These results indicate that data from the VNIR (430–970 nm) can be used to quantify, without a priori knowledge, the total amount of iron minerals and to distinguish ore from shale on vertical mine faces. Numéro de notice : A2013-030 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2012.09.014 En ligne : https://doi.org/10.1016/j.isprsjprs.2012.09.014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32168 [article]

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Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors / R.J. Murphy in IEEE Transactions on geoscience and remote sensing, vol 50 n° 8 (August 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 8 (August 2012) . - pp 3066 - 3080 Titre : Evaluating classification techniques for mapping vertical geology using field-based hyperspectral sensors Type de document : Article/Communication Auteurs : R.J. Murphy, Auteur ; S. Monteiro, Auteur ; S. Schneider, Auteur Année de publication : 2012 Article en page(s) : pp 3066 - 3080 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] Australie occidentale (Australie) [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] classification Spectral angle mapper [Termes IGN] image aérienne [Termes IGN] image hyperspectrale [Termes IGN] mine [Termes IGN] ombre Résumé : (Auteur) Hyperspectral data acquired from field-based platforms present new challenges for their analysis, particularly for complex vertical surfaces exposed to large changes in the geometry and intensity of illumination. The use of hyperspectral data to map rock types on a vertical mine face is demonstrated, with a view to providing real-time information for automated mining applications. The performance of two classification techniques, namely, spectral angle mapper (SAM) and support vector machines (SVMs), is compared rigorously using a spectral library acquired under various conditions of illumination. SAM and SVM are then applied to a mine face, and results are compared with geological boundaries mapped in the field. Effects of changing conditions of illumination, including shadow, were investigated by applying SAM and SVM to imagery acquired at different times of the day. As expected, classification of the spectral libraries showed that, on average, SVM gave superior results for SAM, although SAM performed better where spectra were acquired under conditions of shadow. In contrast, when applied to hypserspectral imagery of a mine face, SVM did not perform as well as SAM. Shadow, through its impact upon spectral curve shape and albedo, had a profound impact on classification using SAM and SVM. Numéro de notice : A2012-381 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2178419 Date de publication en ligne : 03/02/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2178419 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31827 [article]

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Tuning a gravimetric quasigeoid to GPS-levelling by non-stationary least-squares collocation / N. Darbehesti in Journal of geodesy, vol 84 n° 7 (July 2010)  

Public [article]  inJournal of geodesy > vol 84 n° 7 (July 2010) . - pp 419 - 431 Titre : Tuning a gravimetric quasigeoid to GPS-levelling by non-stationary least-squares collocation Type de document : Article/Communication Auteurs : N. Darbehesti, Auteur ; Will E. Featherstone, Auteur Année de publication : 2010 Article en page(s) : pp 419 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes IGN] appariement de données localisées [Termes IGN] collocation par moindres carrés [Termes IGN] géoïde altimétrique [Termes IGN] géoïde gravimétrique [Termes IGN] géoïde local [Termes IGN] nivellement par GPS [Termes IGN] Perth Résumé : (Auteur) This paper addresses implementation issues in order to apply non-stationary least-squares collocation (LSC) to a practical geodetic problem: fitting a gravimetric quasigeoid to discrete geometric quasigeoid heights at a local scale. This yields a surface that is useful for direct GPS heighting. Non-stationary covariance functions and a non-stationary model of the mean were applied to residual gravimetric quasigeoid determination by planar LSC in the Perth region of Western Australia. The non-stationary model of the mean did not change the LSC results significantly. However, elliptical kernels in non-stationary covariance functions were used successfully to create an iterative optimisation loop to decrease the difference between the gravimetric quasigeoid and geometric quasigeoid at 99 GPS-levelling points to a user-prescribed tolerance. Numéro de notice : A2010-301 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-010-0377-3 Date de publication en ligne : 18/04/2010 En ligne : https://doi.org/10.1007/s00190-010-0377-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30495 [article]

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Arthropods in coarse woody debris in jarrah forest and rehabilitated bauxite mines in Western Australia / John M. Koch in Annals of Forest Science, vol 67 n° 1 (January-February 2010)  

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Interpretation of remotely sensed data using guided techniques for land cover analysis / Julien Flack (1995)

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