ERE5.4

UNEXUP is an EIT RawMaterials supported project (Project Number 19160) with the aim to improve and commercialize the robot-based technology developed in the H2020 UNEXMIN project (2016-2019). In UNEXMIN three underwater robot prototypes (UX-1 a,b,c) were built with geoscientific and navigational instruments capable of collecting valuable geological, mineralogical and spatial information from flooded mines without causing harm to the environment, risk to human lives, or high dewatering costs. This technology was tested in five different field trials and proved to be an efficient exploration method to sustainably evaluate the potential for mineral resources in these mines. For example, scanning sonars and structured light systems can map the environment even with near-zero visibility, the visible light cameras allow the identification of structural and geological features, the gamma-ray counter helps to identify minerals with natural radiation, and the pH, EC and water sampler allow the characterization of the waters in these sites.

In UNEXUP (2020-2022) the objective is to further improve this robot-based technology, test it in real-life environments, and commercialize it as an exploration service. The UNEXUP technology will comprise two new robots, which will add to the three UX-1s that were developed in UNEXMIN. These new robots consider the feedback and requirements from potential customers (e.g., mining companies and Geological Surveys) and other stakeholders of the predecessor project.

The first robot, UX-1Neo, is an upscaled version of UX-1, with the same dimensions and functionalities. This robot was built to address the limitations and malfunctions found in the previous line of robots, and it has software improvements that allow reduction of the number of operators, with faster mission setup time, and more efficient data collection and processing. With hardware improvements, it is a lighter, modular robot with better thruster control, an additional camera, and easily swappable batteries. The second robot, UX-2, to be built in 2021, will be a more complex unit with increased modularity, higher TRL, and greater operational depth. The modularity of both robots allow the sharing of some geoscientific instruments that are being developed, such as multispectral camera, water sampling unit, water chemistry measurement, and fluxgate magnetometer. In addition, there will be a rock sampling unit supported by a robotic arm, which will be developed exclusively for UX-2.

The robots will demonstrate their capabilities under real-life environments during the project. A real service-to-client approach is being carried out, and commercial missions have already been scheduled for the UX-1Neo in 2021. Some examples include a 3D inspection of a water well, geoscientific survey of a flooded salt mine, as well as other survey missions under discussion in Europe and worldwide.

Both robots are equipped with navigational and geoscientific instruments to address surveying requirements in flooded mines. However, there is a range of other applications for this technology, including: inspection of water wells and reservoirs, cultural heritage sites, cave exploration, environmental risk evaluation, and many other underwater structures that can benefit from this technology.

How to cite: Pinto, M., Zajzon, N., Bodo, B., Lopes, L., Henley, S., Almeida, J., Aaltonen, J., Rossi, C., and Zibret, G.: UNEXUP, a robotic exploration technology for underground flooded mines, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1368, https://doi.org/10.5194/egusphere-egu21-1368, 2021.

Automation and robotics raise growing interests in the mining industry. If not already a reality, it is no more science fiction to imagine autonomous robots routinely participating in the exploration and extraction of mineral raw materials in the near future. Among the various scientific and technical issues to be addressed towards this objective, this study focuses on the automation of real-time characterisation of rock images captured on the field, either to discriminate rock types and mineral species or to detect small elements such as mineral grains or metallic nuggets. To do so, we investigate the potential of methods from the Computer Vision community, a subfield of Artificial Intelligence dedicated to image processing. In particular, we aim at assessing the potential of Deep Learning approaches and convolutional neuronal networks (CNN) for the analysis of field samples pictures, highlighting key challenges before an industrial use in operational contexts.

In a first initiative, we appraise Deep Learning methods to classify photographs of macroscopic rock samples between 12 lithological families. Using the architecture of reference CNN and a collection of 2700 images, we achieve a prediction accuracy above 90% for new pictures of good photographic quality. Nonetheless we then seek to improve the robustness of the method for on-the-fly field photographs. To do so, we train an additional CNN to automatically separate the rock sample from the background, with a detection algorithm. We also introduce a more sophisticated classification method combining a set of several CNN with a decision tree. The CNN are specifically trained to recognise petrological features such as textures, structures or mineral species, while the decision tree mimics the naturalist methodology for lithological identification.

In a second initiative, we evaluate Deep Learning techniques to spot and delimitate specific elements in finer-scale images. We use a data set of carbonate thin sections with various species of microfossils. The data comes from a sedimentology study but analogies can be drawn with igneous geology use cases. We train four state-of-the-art Deep Learning methods for object detection with a limited data set of 15 annotated images. The results on 130 other thin section images are then qualitatively assessed by expert geologists, and precisions and inference times quantitatively measured. The four models show good capabilities in detecting and categorising the microfossils. However differences in accuracy and performance are underlined, leading to recommendations for comparable projects in a mining context.

Altogether, this study illustrates the power of Computer Vision and Deep Learning approaches to automate rock image analysis. However, to make the most of these technologies in mining activities, stimulating research opportunities lies in adapting the algorithms to the geological use cases, embedding as much geological knowledge as possible in the statistical models, and mitigating the number of training data to be manually interpreted beforehand.   

How to cite: Bouziat, A., Desroziers, S., Koroko, A., Lechevallier, A., Feraille, M., Lecomte, J.-C., and Divies, R.: Towards robots with geologist eyes? Computer vision and Deep Learning approaches to field samples analysis, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-11144, https://doi.org/10.5194/egusphere-egu21-11144, 2021.

Seismic imaging while drilling technology offers possibilities of imaging ahead of the drill-bit, which could be useful for determining when to go from hammer drilling to core drilling. Moreover, seismic images of the surrounding rock can improve geological models which could be then used to guide drilling programs.

A seismic imaging while drilling field test was carried out in August 2020 at the I-EDDA Test Center next to the Epiroc factory in Örebro, which is an outcome of the EIT Raw Materials funded project “Innovative Exploration Drilling and Data Acquisition (I-EDDA)”. The purpose of the test presented here was to determine if the signals from hammer drilling can be used for seismic imaging around the drill-bit. The I-EDDA test site has been extensively investigated with geophysical investigations, geological mapping and diamond core drilling, and it therefore represents an ideal location to perform the proposed feasibility study.

The data were recorded along a west-east oriented line consisting of 45 active 1C vertical geophones with a spacing of about 2 m and the rig located approximately in the middle of the profile. A reference signal, which is usually recorded by the pilot sensor fixed to the top of the drill string to be used to convert geophone recordings to impulsive-like seismic data, was not available. The passive recordings on the surface were thus correlated with the trace from the geophone closest to the rig.

After data pre-processing and cross-correlation, the shot-gathers were vertically stacked over the length of a drill pipe to achieve further signal improvement. A comparison with the results of a modelling study shows certain agreement. However, it has to be noted that the velocity model obtained from earlier studies and used to generate the synthetic data set here is rather a simple one and the noise level in the real data set is still significant, in spite of careful processing. Besides the strong contamination by the rig noise, more typical for data with smaller offsets, the mono-frequency waveform footprints present in the cross-correlograms, which have been observed in similar experiments where a trace from the nearest geophone was used to approximate the bit signal, could also play a role. The recent results from the active seismic studies conducted at the site have not detected any clear reflections within the bedrock, which further hinders the quality assessment of the seismic signal.

This work was partly supported by VINNOVA with the project 2019-04832 titled Integrated Smart Test environment for the mining industry - SMIG. We gratefully acknowledge this financial support.

How to cite: Ivandic, M., Kaslilar, A., and Juhlin, C.: Subsurface seismic imaging with a hammer drilling source at an exploration drilling test center in Örebro, Sweden, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3442, https://doi.org/10.5194/egusphere-egu21-3442, 2021.

Volatile geogenic components, such as CO₂, He, Rn, and Hg⁰, form haloes in soil gas and near-surface air directly above mineral deposits. This contrasts with lithochemical, hydrochemical, and biochemical dispersion haloes that can be laterally displaced or obscured by transported overburden. Mercury vapour surveys have been used in exploration, because Hg occurs in most types of enogenic ore deposit types and is highly mobile. Low background concentrations in the atmosphere (1.2 to 1.5 ng/m³) enable detecting even weak Hg emissions directly above buried ore deposits. In this study, we measured Hg vapour in air 1-50 cm above ground at 15 sites on Vancouver Island, British Columbia, Canada. To evaluate the effectiveness of the method across a range of settings, these sites include different types of known mineralized zones, barren rocks, and faults, both buried and exposed. The direct and continuous analysis via a portable RA-915M mercury analyzer reveals Hg vapour concentrations ranging from 0.5 to 54.4 ng/m³. The highest Hg concentration was observed above tailings at the Bentley Au occurrence, possibly due to the amalgamation technique used for fine gold extraction between late 1800s and early 1900s. Prominent Hg vapour haloes mark shear-hosted Cu-Ag-Au sulphides at Mount Skirt (13.4x background Hg), epithermal Au-Ag-Cu at Mount Washington (8.9x background Hg), and sediment-covered polymetallic volcanogenic massive sulphides at the Lara-Coronation occurrence (4.2 to 6.6x background Hg). Basalt-hosted Cu-Ag-Au sulphide zones at the Sunro past producer are marked by weak Hg vapour anomalies relative to local background. Faults, including the Leech River fault, which was active in the Quaternary, are also marked by weak Hg vapour anomalies. The study confirms that, although the Hg level is influenced by weather, the real-time Hg vapour measurement of near-surface air can instantly delineate mineralized zones and fault structures that are buried under overburden 10s of m thick. In contrast to soil gas sampling, this simple and rapid technique can be applied to mineral exploration and geological mapping under overburden above any type of surface, including outcrops, talus, bogs, water bodies, snow, and permafrost.

How to cite: Rukhlov, A. S., Ootes, L., Hickin, A. S., and Mashyanov, N. R.: Mercury vapour haloes in near-surface air above ore deposits and faults on Vancouver Island, British Columbia, Canada, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-3473, https://doi.org/10.5194/egusphere-egu21-3473, 2021.

Alkaline complexes are an important target for geological exploration, with both scientific and economic interests. They are host to different types of mineral deposits, such as Rare Earths, igneous phosphates, -and K-rich minerals and rocks. In Morocco, the Central High-Atlas (CHA) hosts several transitional to alkaline complexes ranging from Upper Jurassic to Eocene and showing almost all the differentiation terms of transitional to alkaline suites. These alkaline complexes are however poorly explored and their potential in terms of mineral resources is still elusive.

The aim of this research is to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) to discriminate different transitional to alkaline rock lithologies and their associated mineralizations. For that purpose, series of band ratios proven to be sensitive to the silica, mafic, felsic and carbonate contents of transitional to alkaline rocks were applied. Our results show that the major Upper Jurassic magmatic intrusions of Moroccan CHA, such as Anemzi, Inouzane, Tassent, and Tasraft, hold distinct igneous facies, mainly composed of Mafic to felsic rocks. Field and petrographic observations have confirmed the ASTER results and highlighted that these rocks are formed of gabbro to syenite. The later are associated with significant feldspar concentrations, but also host apatite, garnet, and magnetite vein-type ores. Thereafter, field- and petrographic-based data were used as training data to perform a supervised classification allowing to refine the geological mapping of the studied alkaline intrusions.

How to cite: Ahechach, Y., Ouabid, M., Raji, O., Bodinier, J.-L., Amrouch, K., Ouali, H., and Soulaimani, A.: Remote sensing characterization of transitional to alkaline igneous rocks and their potential mineralizations using ASTER data: the Moroccan Central High Atlas case study, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5091, https://doi.org/10.5194/egusphere-egu21-5091, 2021.

Modern mineral exploration is required to be conducted in a sustainable, environmentally friendly and socially acceptable way. Especially for the geochemical exploration on ecologically sensitive areas this poses a challenge because any heavy machinery or invasive methods might cause long-lasting damage to nature. One way of reducing the impact of mineral exploration on the environment during the early stages of exploration is to use surface sampling media, such as upper soil horizons, water, plants and, on high latitudes, also snow. Of these options, snow has several advantages: Sampling and analysing snow is fast and low in costs, it has no impact on the environment, and in wintertime it is ubiquitous and available independent of the ecosystem.
In the “New Exploration Technologies (NEXT)” project*, snow samples were collected in March-April 2019 to evaluate the usage of snow as a sampling material for mineral exploration. The test site was the Rajapalot Au-Co prospect in northern Finland, located 60 km west from Rovaniemi and operated by Mawson Oy. A stratified random sampling strategy was applied to place the sampling stations on the test site. The sampling comprised 94 snow samples and 12 field replicates. The samples were analysed at the GTK Research laboratory using a Nu AttoM single collector inductively coupled plasma mass spectrometry (SC-ICPMS) which returned analytical results for 52 elements at the ppt level. After applying quality control to the data, the elements Ba, Ca, Cd, Cr, Cs, Ga, Li, Mg, Rb, Sr, Tl and V showed good quality and were used in the final data analysis.
Geochemical data of drill cores were used to train a model to predict bedrock geochemistry based on the 12 available element concentrations of snow analysis. Prior to statistical methods, all geochemical data was transformed to log-ratio scores in order to ensure that results are independent of the selection of elements and to avoid spurious correlations (compositional data approach). Results show that snow data provide reasonable predictions of bedrock geochemistry for elements such as Ca, Cr, Li and Mg, but also for elements not used in snow data, such as Mn and Na. This suggests that snow can serve as a lithogeochemical mapping tool for potential geological domains. For the ore related elements Au, Ag, Co, and U the model provided predictions with higher uncertainty. Yet, the pattern of the predicted values of ore related elements show that snow can also be used to delineate prospective areas for continuing exploration with more sensitive methods.
*) This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 776804.

How to cite: Pospiech, S., Taivalkoski, A., Lahaye, Y., Sarala, P., Kinnunen, J., and Middleton, M.: Snow as environmentally low-impact sampling media for mineral exploration - a case study from Northern Finland, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-5174, https://doi.org/10.5194/egusphere-egu21-5174, 2021.

NW Iberia hosts a substantial number of mineral resources. Among them, gold (Au) acquired particular relevance since Antiquity, representing one of the largest Roman Au mining provinces in Europe. While primary deposits associated with orogenic Au have been widely studied in the past years, the Plio-Quaternary Raña Au-bearing placer deposits of the western Duero Basin have received little attention. Besides, the different morphology of Au particles suggests complex processes that may have been responsible for the secondary formation of colloidal particles and Au growth grains from complex geochemical soil interactions and biological activity. In this context, exploring the mechanism by means these secondary deposits developed may contribute to understanding the source of Au (extrinsic or intrinsic factors that rule in within Raña deposits) and the formation of potential mineral exploration sectors. This paper outlines the geochemical analysis of a Cenozoic Raña-like deposit in the Jamuz valley (León), where the source of Au and the main characteristics are established. The correlation matrix showed notable relationships between Au, Fe, Na, K, Ca, Pb and As, among the most important. High values in Fe and As provides direct evidence of Au precipitation. Likewise, a non-linear correlation was found between Au-Na, and Au-Ca, suggesting a direct link to soil formation processes. Finally, the presence of apparent differences in grain roundness and the particles' characteristics ranging from monomineral angular Au to polymineral rounded-shaped particles points towards a complex process affecting the Raña deposits. The ubiquitous rubefaction and top-bottom leaching activity produced during rainwater percolation aided by the extreme drainage affecting this conglomeratic formation have often been argued to be responsible for the transformation of mineral phases in soils. The presence of secondary silicification processes and pH drop due to biological reactions (i.e., presence of P) may have been a triggering mechanism for digestion and reprecipitation of Au colloids in these sediments. Our results have outstanding implications on the mechanisms that may determine the Au enrichment of certain levels within the Raña deposits of the western Duero Basin.

This work was funded by the wine company “Fuentes del Silencio”.

How to cite: Fernández-Lozano, J., Esbrí, J. M., Garrido, I., Carrasco, R. M., Pedraza, J., Bernardo-Sánchez, A., and Higueras, P.: Unravelling the origin of placer gold: A case study on the largest Roman gold mining sector of NW Spain (Jamuz, León), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-10571, https://doi.org/10.5194/egusphere-egu21-10571, 2021.

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’ 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’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.  

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  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 “invisible”. Altogether, ROBOMINERS can contribute to solve some of the main issues that make mining’s social license to operate so difficult to get in Europe: land-use, environmental limitations, and socio-economic aspects.

How to cite: Bodo, B., Lopes, L., Rossi, C., Stasi, G., Burlet, C., Henley, S., Correia, V., Pinkse, T., Kot-Niewiadomska, A., Aaltonen, J., Sifferlinger, N., Cristo, N., Hartai, É., Zibret, G., Horvath, J., and Ristolainen, A.: A new mining life for non-feasible mineral deposits?, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12115, https://doi.org/10.5194/egusphere-egu21-12115, 2021.

Ambient noise surface wave tomography (ANSWT) is an environmentally friendly and cost-effective technique for subsurface imaging. In this study, we used natural (low-frequency) and anthropogenic (high-frequency) noise sources to map the velocity structure of the Marathon Cu-PGE deposit (Ontario, Canada) to a depth of 1 km. The Marathon deposit is a circular (ø = 25 km) alkaline intrusion comprising gabbros at the rim and an overlying series of syenites in the centre. Cu-PGE mineralisation is hosted by gabbros close to the inward-dipping footwall of the intrusion. The country rocks are Archaean volcanic breccias that are seismically slower than the gabbros, and similar in velocity to the syenites. We used ANSWT to image the footwall contact that controls the location of the mineralisation.

An array of 1024 vertical-component receivers were deployed for 30 days to record ambient noise required for surface wave analysis. Two overlapping grids were used: a 200 m x 6040 m dense array with node spacing of 50 m, and a 2500 m x 4000 m sparse array with node spacing of 150 m.  The signal was down-sampled to 50 Hz, divided into segments of 30 minutes, cross-correlated and stacked. Surface wave analysis was conducted over the dense array and the sparse array data. We considered the fundamental mode of Rayleigh wave propagation for our frequency-wavenumber (F-K) analysis and focused on the phase velocity variation in the high-frequency ambient noise signal (up to 22 Hz). We reconstructed the shallow structure with progressively increased resolution using surface wave dispersion curves extracted from receiver arrays divided into segments of variable lengths. Several average dispersion curves were computed from individual dispersion curves belonging to different seismic lines. Each average dispersion curve was inverted to obtain S-wave velocity models using an McMC transdimensional Bayesian approach.

The tomographic images reveal a shallow high-velocity anomaly, which we interpret as being related to the gabbro intrusion that hosts the mineralization. The large-wavelength structures in the S-wave velocity models are relatively consistent with the geological structures inferred from surface mapping and drill core data. These results show that the ANSWT, focused on the high-frequency signal provided by anthropogenic noise sources, is an efficient technique for imaging “shallow" (1 km depth) geological structures in a mineral exploration context. 

How to cite: Teodor, D., Beard, C., Pinzon-Rincon, L. A., Mordret, A., Lavoué, F., Beaupretre, S., Boué, P., and Brenguier, F.: High-frequency ambient noise surface wave tomography at the Marathon PGE-Cu deposit (Ontario, Canada), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13152, https://doi.org/10.5194/egusphere-egu21-13152, 2021.

Geological models, as 3-D representations of subsurface structures and property distributions, are used in many economic, scientific, and societal decision processes. These models are built on prior assumptions and imperfect information, and they often result from an integration of geological and geophysical data types with varying quality. These aspects result in uncertainties about the predicted subsurface structures and property distributions, which will affect the subsequent decision process.

We discuss approaches to evaluate uncertainties in geological models and to integrate geological and geophysical information in combined workflows. A first step is the consideration of uncertainties in prior model parameters on the basis of uncertainty propagation (forward uncertainty quantification). When applied to structural geological models with discrete classes, these methods result in a class probability for each point in space, often represented in tessellated grid cells. These results can then be visualized or forwarded to process simulations. Another option is to add risk functions for subsequent decision analyses. In recent work, these geological uncertainty fields have also been used as an input to subsequent geophysical inversions.

A logical extension to these existing approaches is the integration of geological forward operators into inverse frameworks, to enable a full flow of inference for a wider range of relevant parameters. We investigate here specifically the use of probabilistic machine learning tools in combination with geological and geophysical modeling. Challenges exist due to the hierarchical nature of the probabilistic models, but modern sampling strategies allow for efficient sampling in these complex settings. We showcase the application with examples combining geological modeling and geophysical potential field measurements in an integrated model for improved decision making.

How to cite: Wellmann, F., de la Varga, M., Güdük, N., von Harten, J., Stamm, F., Liang, Z., and Moulaeifard, s. M.: Probabilistic Machine Learning for improved Decision-making with 3-D Geological Models, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-14771, https://doi.org/10.5194/egusphere-egu21-14771, 2021.

Gadag schist belt, India is known for sulphide-gold mineralization. In the study area mineralization is controlled structurally and lithologically. In this context, Airborne Visible-Infrared Imaging Spectrometer - Next Generation (AVIRIS-NG) Visible Near InfraRed (VNIR) - Shortwave Infrared (SWIR) bands were utilized to derive alteration zones and structures present in the study area. Lithological boundaries have also been updated using AVIRIS-NG VNIR-SWIR bands derived images enhancement products i.e. Minimum Noise Fraction (MNF) and False Colour Composite (FCC). Further, image spectra of alteration zones (Hydrous mineral etc.) derived from AVIRIS-NG calibrated VNIR-SWIR bands were compared with the standard corresponding reference library spectra (USGS, JPL spectral library). These image spectra have been utilized to demarcate the alteration zones using the Matched Filtering spectral mapping method. Structures were demarcated using high pass (HP) filtered image and FCC images. Low pass (LP) filter image and along with MNF & FCC image composite were utilized to update the lithological boundaries in the study area.

Ground gravity data has also been processed to derive the subsurface evidences relevant to the deposit in the present study area. Subsurface structures which are responsible for the transportation of mineral rich fluid in the near subsurface are delineated using the gravity data derived products. Apart from this, basement depths are also derived from the gravity data which are being utilized for the validation as well as to further precise the locations of mineral deposits.  These subsurface structures (gravity data), lithology, lineament density and alteration zones are very important evidential layers which have been integrated using fuzzy logic integration techniques to identify potential zones of gold-sulphide mineralization in the present study area. The prospective zones are validated using the secondary data and basement depth derived from the gravity data.

For similar kind of gold-sulphide mineralization, AVIRIS-NG data and Gravity data can be used to derive the important evidential layers in any part of the world. There are only few studies where such integration approach has been utilized to explore new potential zones of gold sulphide mineralization. 

Keywords: AVIRIS-NG, VNIR-SWIR, alteration, MNF, FCC, Gravity, Basement Depth

How to cite: Rani, K.: Mineral Prospectivity Modeling using AVIRIS-NG VNIR-SWIR data and Gravity data for Gold-Sulphide mineralization in parts of GADAG schist belt, Karnataka, India, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15900, https://doi.org/10.5194/egusphere-egu21-15900, 2021.

During the last decades, carbonatites and associated rocks have received increased interest from mining companies and the scientific community. They represent a classic source of a variety of critical elements required by certain emerging technologies and industries such as niobium, rare earth elements (REE), and phosphorus. Morocco like many other countries have several Alkaline igneous complexes, however, their potential in terms of REE-P-rich carbonatites is poorly explored and needs to be investigated. This study is an attempt to develop an advanced exploration tool for the detection and mapping of these rocks using remote sensing.  Preliminary investigations were focused on the Oulad Dlim massif at the western Reguibat Shield (Southeast of Dakhla province) where several carbonatite structures were reported, including Gleibat Lafhouda, Twihinate, Lamlaga, Lahjayra. Advanced Spaceborne Thermal and Reflection Radiometer (ASTER) data were used to: (i) identify and map carbonatites and associated rocks liable to contain REE-P mineralization, (ii) investigate their spectral features, and produce predictive maps. Several image processing techniques, have been performed including band ratio, color composite image, principal component analysis and minimum noise fraction. The combination of these techniques appears to more effectively detect carbonatites and associated rocks. The effectiveness of this approach was verified using field investigation, in-situ geochemical analysis with portable X-ray fluorescence, and petrography. The field data were used to train classifiers to better delineate the spatial distribution of the different lithological facies. The results are generally consistent with available geological maps indicating that this approach can be satisfactorily applied in the early stages of geological exploration.

How to cite: Malainine, C. E., Raji, O., Ouabid, M., Khouakhi, A., Bodinier, J.-L., Parat, F., and El Messbahi, H.: Geological mapping of carbonatites and related ores from the Oulad Dlim massif (Dakhla Province, Morocco) using remote sensing, portable X-ray fluorescence, and mineralogical data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-15929, https://doi.org/10.5194/egusphere-egu21-15929, 2021.