Identification of pegmatites zones in Muiane and Naipa (Mozambique) from Sentinel-2 images, using band combinations, band ratios, PCA and supervised classification

Remote sensing has been widely used in Geological Sciences for different applications, such as to identify geological and mineralogical objects and surface alteration changes. This study aimed to analyze the Sentinel-2 potential to detect pegmatite bodies and associated alteration zones in Muiane and Naipa in Mozambique. Different remote sensing techniques were applied to a Sentinel-2 image: RGB combinations, band ratios, principal component analysis (PCA), and supervised image classification algorithms such as the Maximum Likelihood Classifier (MLC) and Support Vector Machine (SVM). MLC was used as a benchmark classifier to evaluate the performance of SVM because MLC is the predominant algorithm employed in remote sensing classification studies. For that, several statistical metrics based on the confusion matrices were computed, namely accuracy, Kappa index, precision, recall, and f-score, among others. This study allows identifying the location of pegmatites by direct identification and segregating between hydrothermally altered zones and non-altered areas through remote sensing data/techniques, supported by field data. The field campaigns allowed for validating the results obtained and verifying the pegmatites identified using Sentinel-2 data that were not previously mapped. Moreover, reflectance spectroscopy studies in the laboratory were conducted on the samples collected in the field campaigns allow to validate the adequacy of the methodology proposed in this study. The results show that the precise identification of pegmatite targets requires a high spatial resolution such as Sentinel-2 images. Thus, with the integration of high spatial and spectral resolution data, a potential level of precision and accuracy can be achieved in the study areas

Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites

Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda–Almendra region (Spain–Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM’s on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed.The authors would like to thank the financial support provided by FCT—Fundação para a Ciência e a Tecnologia, I.P., with the ERA-MIN/0001/2017—LIGHTS project. The work was also supported by National Funds through the FCT project UIDB/04683/2020—ICT (Institute of Earth Sciences). Joana Cardoso-Fernandes is financially supported within the compass of a Ph.D. Thesis, ref. SFRH/BD/136108/2018, by national funds from MCTES through FCT, and co-financed by the European Social Fund (ESF) through POCH—Programa Operacional Capital Humano. The Spanish Ministerio de Ciencia, Innovacion y Universidades (Project RTI2018-094097-B-100, with ERDF funds) and the University of the Basque Country (UPV/EHU) (grant GIU18/084) also contributed economically

Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites

Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda-Almendra region (Spain-Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM's on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed

Detecting Lithium (Li) Mineralizations from Space: Current Research and Future Perspectives

Optical and thermal remote sensing data have been an important tool in geological exploration for certain deposit types. However, the present economic and technological advances demand the adaptation of the remote sensing data and image processing techniques to the exploration of other raw materials like lithium (Li). A bibliometric analysis, using a systematic review approach, was made to understand the recent interest in the application of remote sensing methods in Li exploration. A review of the application studies and developments in this field was also made. Throughout the paper, the addressed topics include: (i) achievements made in Li exploration using remote sensing methods; (ii) the main weaknesses of the approaches; (iii) how to overcome these difficulties; and (iv) the expected research perspectives. We expect that the number of studies concerning this topic will increase in the near future and that remote sensing will become an integrated and fundamental tool in Li exploration

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.http://sajg.geoscienceworld.orgam2021Geography, Geoinformatics and MeteorologyGeolog

A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.</jats:p

Mapeo de redes de fracturas mediante imágenes Landsat-8 OLI en la zona minera de Jbel Tijekht en el Anti-Atlas oriental de Marruecos

Jbel Tijekht is one of the most important geological structures of the Ougnat-Ouzina ridge in the Eastern Anti-Atlas. This crescent-shaped massif was affected by a network fractures that is visible at different scales. It is particularly rich in numerous mineralized veins of barite, and is associated with other minerals (e.g. pyrite, chalcopyrite, sphalerite and galena). In order to study fracture systems in the mining zone of Jbel Tijekht, we opted for a combination of remote sensing and field investigation that became an important tool for fracture mapping and mineral exploration. This work presents a methodological approach to detect structural lineaments. For this purpose, various techniques were applied to the Landsat 8 image to improve the visibility of linear structures. After the radiometric and atmospheric corrections, the colors composites and directional filters applied to the Principal component (PC1) allow for the establishment of a lineaments map of Jbel Tijekht. The validation and the correction of lineaments are based on preexisting documents combined with field observations. Statistical analysis of the lineament map allows for identification of at least three directional fracture systems with average NS, NE-SW, and ENE-WSW orientations. NS and NE-SW systems show a high density in the largest part of the study area. These results clearly overlap different tectonic structures and existing veins. It allowed for the establishment of a geological link between lithology, fractures systems and mineralization. The fracture density can be attributed to the last variscan brittle phases, reflecting the rheology of rock units; the high fracture density is observed in competent rocks such as the Tabanit sandstones. These zones constitute a favorable area for mineralization deposits.El Jbel Tijekht es una de las estructuras geológicas más importantes de la cordillera de Ougnat-Ouzina en el Anti-Atlas Oriental. Este macizo en forma de media luna fue afectado por una red de fracturas que es visible a diferentes escalas. Es particularmente rico en vetas mineralizadas de barita y está asociado con otros minerales (por ejemplo, pirita, calcopirita, esfalerita y galena). Para estudiar los sistemas de fracturas en la zona minera de Jbel Tijekht, optamos por una combinación de teledetección e investigación de campo que se convirtió en una importante herramienta para la cartografía de las fracturas y la exploración de minerales. Este trabajo presenta un enfoque metodológico para detectar los lineamientos estructurales. Para ello, se aplicaron diversas técnicas a la imagen del Landsat 8 para mejorar la visibilidad de las estructuras lineales. Tras correcciones radiométricas y atmosféricas, los colores compuestos y los filtros direccionales aplicados al componente principal (PC1) se pudo establecer un mapa de lineamientos para Jbel Tijekht. La validación y corrección de estos lineamientos se basan en documentos preexistentes combinados con observaciones de campo. El análisis estadístico del mapa de lineamientos permite la identificación de al menos tres sistemas de fractura direccional con orientaciones promedio NS, NE-SW y ENE-WSW. Los sistemas NS y NE-SW muestran una alta densidad en la mayor parte del área de estudio. Estos resultados claramente se superponen a diferentes estructuras tectónicas y a las vetas existentes. Esto permitió establecer un vínculo geológico entre la litología, los sistemas de fracturas y la mineralización. La densidad de fracturas puede atribuirse a las últimas fases de fragilidad del orógeno varisco, lo que refleja la reología de las unidades de roca; la alta densidad de fractura se observa en las rocas competentes como las areniscas de Tabanit. Estas zonas constituyen un área favorable para los depósitos de mineralización

Predictive lithological mapping through machine learning methods: a case study in the Cinzento Lineament, Carajás Province, Brazil

The Cinzento Lineament (Carajás Mineral Province) represents a complex deformational system with great associated mineral potential, mainly for IOCG deposits. However, the tropical vegetation of the Amazon rainforest considerably limits the number of outcrops available for systematic geological mapping. Therefore, the use of remote data such as airborne geophysics and remote sensing is essential to provide a reliable geological map. The airborne magnetometric data to define lithological units and its boundaries is a challenge, especially in regions with low magnetic latitude and/or remanent magnetization. In this work, we proposed an approach using Magnetization Vector Inversion (MVI) to map the distribution of the magnetic susceptibility, in order to replace techniques such as pole reduction and total gradient. We applied the Random Forest algorithm (supervised Machine Learning algorithm) to recognize patterns in remote data and improve the current mapped lithological units. With 1400 training samples (2.5% of the total samples), we produced two Predictive lithological maps: a first with remote data only and a second with remote data and spatial coordinates. We evaluate the advantages and disadvantages of each Predictive map, and we conclude that both maps need to be analyzed together for the refinement of the current geological map. These predictive maps represent a powerful tool to combine remote data to improve current geological maps, or even generate the first-pass geological map for regions with scarce geological knowledge

A review of laser scanning for geological and geotechnical applications in underground mining

Laser scanning can provide timely assessments of mine sites despite adverse challenges in the operational environment. Although there are several published articles on laser scanning, there is a need to review them in the context of underground mining applications. To this end, a holistic review of laser scanning is presented including progress in 3D scanning systems, data capture/processing techniques and primary applications in underground mines. Laser scanning technology has advanced significantly in terms of mobility and mapping, but there are constraints in coherent and consistent data collection at certain mines due to feature deficiency, dynamics, and environmental influences such as dust and water. Studies suggest that laser scanning has matured over the years for change detection, clearance measurements and structure mapping applications. However, there is scope for improvements in lithology identification, surface parameter measurements, logistic tracking and autonomous navigation. Laser scanning has the potential to provide real-time solutions but the lack of infrastructure in underground mines for data transfer, geodetic networking and processing capacity remain limiting factors. Nevertheless, laser scanners are becoming an integral part of mine automation thanks to their affordability, accuracy and mobility, which should support their widespread usage in years to come