Classifiers for modeling of mineral potential

[Extract] Classification and allocation of land-use is a major policy objective in most countries. Such an undertaking, however, in the face of competing demands from different stakeholders, requires reliable information on resources potential. This type of information enables policy decision-makers to estimate socio-economic benefits from different possible land-use types and then to allocate most suitable land-use. The potential for several types of resources occurring on the earth's surface (e.g., forest, soil, etc.) is generally easier to determine than those occurring in the subsurface (e.g., mineral deposits, etc.). In many situations, therefore, information on potential for subsurface occurring resources is not among the inputs to land-use decision-making [85]. Consequently, many potentially mineralized lands are alienated usually to, say, further exploration and exploitation of mineral deposits. Areas with mineral potential are characterized by geological features associated genetically and spatially with the type of mineral deposits sought. The term 'mineral deposits' means .accumulations or concentrations of one or more useful naturally occurring substances, which are otherwise usually distributed sparsely in the earth's crust. The term 'mineralization' refers to collective geological processes that result in formation of mineral deposits. The term 'mineral potential' describes the probability or favorability for occurrence of mineral deposits or mineralization. The geological features characteristic of mineralized land, which are called recognition criteria, are spatial objects indicative of or produced by individual geological processes that acted together to form mineral deposits. Recognition criteria are sometimes directly observable; more often, their presence is inferred from one or more geographically referenced (or spatial) datasets, which are processed and analyzed appropriately to enhance, extract, and represent the recognition criteria as spatial evidence or predictor maps. Mineral potential mapping then involves integration of predictor maps in order to classify areas of unique combinations of spatial predictor patterns, called unique conditions [51] as either barren or mineralized with respect to the mineral deposit-type sought

Spatial mathematical models for mineral potential mapping

A key problem in spatial-mathematical-model-based mineral potential mapping is the selection of appropriate functions that can effectively approximate the complex relationship between target mineral deposits and recognition criteria. This paper evaluates a series of spatial mathematical models based on different linear and non-linear functions by applying them to base-metal potential mapping of the Aravalli province, western India. Linear models applied are an extended weights-of-evidence model and a hybrid fuzzy weights-of evidence model, while non-linear models are knowledge and data-driven fuzzy models, a neural network model, a hybrid neuro-fuzzy model and an augmented naive Bayesian classifier model. The parameters of the knowledge-driven fuzzy model are estimated from the expert knowledge, while those of the neural network and Bayesian classifier model are estimated from the data. The two hybrid models use both expert knowledge and data for parameter estimation.\ud \ud As compared to the linear models, the non-linear models generally perform better in predicting the known base-metal deposits in the study area. Although the linear models do not fit the data as efficiently as the non-linear models, they are easier to implement using basic GIS functionalities and their parameters are more amenable to geoscientific interpretation. In addition, the linear models are less susceptible to the curse of dimensionality as compared to non-linear models, which makes them more suitable for applications to mineral potential mapping of the areas where there is a paucity of training mineral deposits. The hybrid models that conjunctively use both knowledge and data for parameter estimation generally perform better than purely knowledge-driven or purely data-driven models

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping

Introduction to the Special Issue: GIS-based mineral potential modelling and geological data analyses for mineral exploration

This introduction provides an overview of the procedures involved in mineral potential modelling. The papers included in this Special Issue are also summarized

A continent-wide study of Australia's uranium potential. Part I: GIS-assisted manual prospectivity analysis

This paper describes the approach to, and outcomes of, a manual analysis (i.e., a cognitive assessment of spatial and non-spatial data) of the uranium potential of 90 geological regions in Australia. For this analysis, the 14 principal uranium deposit types recognized by the International Atomic Energy Agency were grouped in six uranium systems models (i.e., surficial, sedimentary, igneous-related, metamorphic/metasomatic, unconformity-related, and vein-related uranium systems) on the basis of similar genetic processes, environments of ore formation and ingredients mappable at the regional to continent scale. The newly proposed uranium systems models are structured according to the mineral systems approach and focus on the critical mineralization processes that must occur for a uranium deposit to form in a particular region. Our manual prospectivity analysis employed this approach to assess the probability of the critical genetic processes having occurred in each geological region. In this semi-quantitative, probabilistic evaluation, technical, quality and opportunity ranking schemes were used to rank each geological region based on the probability of occurrence of and potential for high-quality uranium deposits and opportunity for securing prospective ground.Based on this assessment, the geological regions with the greatest potential for discovery of potentially recoverable uranium resources are the Ashburton, Broken Hill, Litchfield, McArthur, Money Shoal, Murphy, Paterson, Pine Creek and Northeast Tasmania regions (i.e., quality ranking of 10.0), the Gawler and Polda regions (i.e., 9.0), and the Amadeus, Georgetown, Stuart, Tanami regions (i.e., 8.1). Most of these regions contain known unconformity-related or sandstone-hosted uranium deposits, although some of them are pure conceptual plays that have received relatively little attention in terms of uranium exploration. Maps based on the numerical output of the prospectivity analysis helped to inform area selection decisions and detailed follow-up studies, and focus time and resources. The template developed in this study can easily be modified to suit prospectivity analyses for other metals or a similar investigation in another country. As illustrated in Part II, the best possible approach to a complex, continent-wide prospectivity analysis is to harness the strengths of both manual and automated (i.e., sophisticated computational techniques applied to spatial data) approaches as these methodologies essentially address each other's limitations

A new method for spatial centrographic analysis of mineral deposit clusters

A centrographic method for analysing mineral deposit clusters is illustrated using the komatiite-hosted Kambalda nickel sulphide deposit cluster, Yilgarn craton, Western Australia. In this method, the standard distance circle divides the cluster into a more endowed inner part and a less endowed peripheral part. The standard deviational ellipse, another centrographic object, depicts the preferred northwest–southeast trend of nickel orebodies at Kambalda. Weighted centrography shows that nickel endowment is greater in the eastern than western part of the cluster. The spatio-geometric interaction of the circle and ellipse splits the cluster into several partitions. The relative concentration of nickel orebodies or endowment within a partition in relation to their concentration within the entire cluster is termed ‘capture efficiency’. Komatiite areal trace exhibits higher nickel orebody capture efficiency than spatio-geometric partitions; however, some spatio-geometric partitions exhibit nickel endowment capture efficiencies comparable to that of komatiite. Furthermore, nickel orebody and endowment capture efficiencies of komatiite are elevated only within the standard distance circle. These results suggest that at Kambalda, (i) the standard distance circle is a prime window for understanding the komatiite-hosted nickel system, and (ii) spatio-geometric partitions are plausible locales for spatial analysis of nickel orebodies and endowment. The proposed centrographic method is potentially useful in mineral resource estimations and mineral exploration targeting

A new method for spatial centrographic analysis of mineral deposit clusters

A centrographic method for analysing mineral deposit clusters is illustrated using the komatiite-hosted Kambalda nickel sulphide deposit cluster, Yilgarn craton, Western Australia. In this method, the standard distance circle divides the cluster into a more endowed inner part and a less endowed peripheral part. The standard deviational ellipse, another centrographic object, depicts the preferred northwest–southeast trend of nickel orebodies at Kambalda. Weighted centrography shows that nickel endowment is greater in the eastern than western part of the cluster. The spatio-geometric interaction of the circle and ellipse splits the cluster into several partitions. The relative concentration of nickel orebodies or endowment within a partition in relation to their concentration within the entire cluster is termed ‘capture efficiency’. Komatiite areal trace exhibits higher nickel orebody capture efficiency than spatio-geometric partitions; however, some spatio-geometric partitions exhibit nickel endowment capture efficiencies comparable to that of komatiite. Furthermore, nickel orebody and endowment capture efficiencies of komatiite are elevated only within the standard distance circle. These results suggest that at Kambalda, (i) the standard distance circle is a prime window for understanding the komatiite-hosted nickel system, and (ii) spatio-geometric partitions are plausible locales for spatial analysis of nickel orebodies and endowment. The proposed centrographic method is potentially useful in mineral resource estimations and mineral exploration targeting

Managing uncertainty in exploration targeting

Mineral exploration is one of the best examples of a business run by judgement under conditions of extreme uncertainty. A study of manual targeting exercises over several groups, on several continents has revealed that targets derived by human-data interaction are fraught with systemic uncertainties dominated by the mineralisation model used (reflecting the preferences/experience of the explorer), how this is translated to a targeting model, and the inability to systematically apply the targeting model over geoscience datasets. Targets generated tend to show some clustering between groups, usually towards areas of outcrop or known mineralisation (a problem when the best opportunities are likely under cover), but very different spreads in ranking. The stochastic uncertainties of the data are important but secondary to these systemic uncertainties. \ud \ud Automated prospectivity analysis methods applied in GIS, although affected by systemic uncertainties in the selection of predictor maps, can partially mitigate the biases of human data interaction but in turn are severely affected by the stochastic uncertainties.\ud \ud Examples from several terranes are used to illustrate that a combination of manual and automated approaches can best manage these uncertainties and enhance the confidence of mineral exploration targets. Keys to applying this approach are: (1) creation of appropriate derived datasets and predictor maps to overcome stochastic uncertainty in areas of cover or poor quality data, (2) a mineral systems approach in generating targeting models, (3) application of manual targeting, followed by automated knowledge- and data-driven approaches in GIS, and (4) final refinement of manual targeting for final target decisions

CNN-Based Super-Resolution of Hyperspectral Images

International audienceSingle-image super-resolution (SISR) techniques attempt to reconstruct the finer resolution version of a given image from its coarser version. In the SISR of hyperspectral data sets, the simultaneous consideration of spectral bands is crucial for ensuring the spectral fidelity. However, the high spectral resolution of these data sets affects the performance of conventional approaches. This research proposes the design of 3-D convolutional neural network (CNN)-based SISR architectures that can map the spatial-spectral characteristics of hypercubes to a finer spatial resolution. The proposed approaches facilitate the simultaneous optimization of sparse codes and dictionaries with regard to the super-resolution objective. Our main hypothesis is that the consideration of spectral aspects is essential for the spatial enhancement of hyperspectral images. Also, we propose that the regularized deconvolution of a coarser-scale hypercube, using learned 3-D filters, yields the required high-resolution version. Based on these hypotheses, a convolution-deconvolution framework is proposed to super-resolve the hypercubes in parallel with the reconstruction of a set of regularizing features. Novel sparse code optimization sub-networks proposed in this article give better performance than the existing strategies. The endmember similarities and hyperspectral image prior are considered while designing the proposed loss functions. In order to improve the generalizability, a collaborative spectral unmixing strategy is employed to refine the spectral base of the super-resolved result. The spatial-spectral accuracy of the super-resolved hypercubes, in terms of the validity of regularizing features and endmembers, is explored to devise an optimal ensemble strategy. The experiments, over different data sets, confirm better accuracy of the proposed frameworks compared to the prominent approaches

Indian carbonatites in the global tectonic context

Abstract Chrono-tectonic settings of the carbonatite occurrences of India are reviewed with a focus on the “big picture” of carbonatite emplacements in the Indian plate in relation to global tectonic events associated with the amalgamation and breakup of supercontinents. Four chrono-tectonic domains, namely, Southern domain, Southeastern domain, Northeastern domain and Northwestern domain, are delineated based on the geographical distribution, tectonic settings and temporal relationships amongst the carbonatite complexes. The Southern domain comprises two sub-domains — Paleoproterozic and Neoproterozoic. The Paleoproterozoic sub-domain is related to extension due to relaxation after the Southern Granulite Terrain-Dharwar accretion, while the Neoproterozoic sub-domain is related to rifting related to the fragmentation of Rodinia. The Southeastern domain is related to the Mesoproterozoic fragmentation of Columbia. The Northeastern domain is related to the Mid-Cretaceous breakup of Greater India from Australia-Antarctica driven by the Kerguelen mantle plume that also produced the Rajmahal-Sylhet Large Igneous Province (LIP). The Northwestern domain is related to the Late-Cretaceous Indo-Seychelles-Madagascar split and the passage of Greater India over the reunion hotspot, which also produced the Deccan LIP