FUCOM-MOORA and FUCOM-MOOSRA: new MCDM-based knowledge-driven procedures for mineral potential mapping in greenfields

AbstractIn this study, we present the application of two novel hybrid multiple-criteria decision-making (MCDM) techniques in the mineral potential mapping (MPM), namely FUCOM-MOORA and FUCOM-MOOSRA, as robust computational frameworks for MPM. These were applied to a set of exploration targeting criteria of skarn. The multi-objective optimization method on the basis of ratio analysis (MOORA) and the multi-objective optimization on the basis of simple ratio analysis (MOOSRA) approaches are used to prioritize and rank individual cells. What makes MOORA and MOOSRA more reliable compared to many other methods is the fact that the optimizations procedure is applied to calculate the prospectivity score of individual unit cells. This reduces the uncertainty stemming from erroneous mathematical calculations. The full consistency method (FUCOM), on the other hand, is useful for assigning weights to the spatial proxies. The FUCOM method, as a pairwise comparison method, reduces a large number of pairwise comparisons of similar and popular approaches such as analytic hierarchy process (AHP) with $$n\left( {n - 1} \right)/2$$ n n - 1 / 2 and the best–worst method (BWM) with $$2n - 3$$ 2 n - 3 number of pairwise comparisons with $$n - 1$$ n - 1 which leads to a less time-consuming and more consistent performance compared with AHP and BWM. These were applied to a set of exploration targeting criteria of skarn iron deposits from Central Iran. Two potential maps were retrieved from the procedures applied, the comparison of which using correct classification rates and field checks revealed the superiority of FUCOM-MOOSRA over the FUCOM-MOORA

Unconformity-type uranium systems: a comparative review and predictive modelling of critical genetic factors

A review of descriptive and genetic models is presented for unconformity-type uranium deposits with particular attention given to spatial representations of key process components of the mineralising system and their mappable expressions. This information formed the basis for the construction of mineral potential models for the world's premier unconformity-style uranium provinces, the Athabasca Basin in Saskatchewan, Canada (>650,000 t U3 O8), and the NW McArthur Basin in the Northern Territory, Australia (>450,000 t U3 O8). A novel set of ‘edge’ detection routines was used to identify high-contrast zones in gridded geophysical data in support of the mineral potential modelling. This approach to geophysical data processing and interpretation offers a virtually unbiased means of detecting potential basement structures under cover and at a range of scales. Fuzzy logic mineral potential mapping was demonstrated to be a useful tool for delineating areas that have high potential for hosting economic uranium concentrations, utilising all knowledge and incorporating all relevant spatial data available for the project area. The resulting models not only effectively ‘rediscover’ the known uranium mineralisation but also highlight several other areas containing all of the mappable components deemed critical for the accumulation of economic uranium deposits. The intelligence amplification approach to mineral potential modelling presented herein is an example of augmenting expert-driven conceptual targeting with the powerful logic and rationality of modern computing. The result is a targeting tool that captures the current status quo of geospatial and exploration information and conceptual knowledge pertaining to unconformity-type uranium systems. Importantly, the tool can be readily updated once new information or knowledge comes to hand. As with every targeting tool, these models should not be utilised in isolation, but as one of several inputs informing exploration decision-making. Nor should they be regarded as ‘treasure maps’, but rather as pointers towards areas of high potential that are worthy of further investigation

Application of random-forest machine learning algorithm for mineral predictive mapping of Fe-Mn crusts in the World Ocean

Mineral prospectivity mapping constitutes an efficient tool for delineating areas of highest interest to guide future exploration. Multiple knowledge-driven approaches have been applied for the creation of prospectivity maps for deep-sea ferromanganese (Fe-Mn) crusts over the last decades. The results of a data-driven approach making use of an extensive data collection exercise on occurrences of Fe-Mn crusts in the World Ocean and recent increase in global marine datasets are presented. A Random Forest machine learning algorithm is applied, and results compared with previously established expert-driven maps. Optimal predictive conditions for the algorithm are observed for (i) a forest size superior to a hundred trees, (ii) a training dataset larger than 10%, and (iii) a number of predictors to be used as nodes superior to two. The confusion matrix and out-of-bag errors on the remaining unused data highlight excellent predictive capabilities of the trained model with a prediction accuracy for Fe-Mn crusts of 87.2% and 98.2% for non-crusts locations, with a Kohen’s K index of 0.84, validating its application for prediction at the World scale. The slope of the seafloor, sediment thickness, sediment type, biological productivity, and abyssal mountain constitute the five strongest explanatory variables in predicting the occurrence of Fe-Mn crusts. Most ‘hand-drawn’ knowledge-driven prospective areas are also considered prospective by the random forest algorithm with notable exceptions along the coast of the American continent. However, poor correlation is observed with knowledge-driven GIS-based criterion mapping as the Random Forest considers un-prospective most target areas from the GIS approach. Overall, the Random Forest prediction performs better in predicting a high chance of Fe-Mn crust occurrence in ISA licensed area than the GIS approach, which constitutes an external validation of the predictive quality of the random forest model

A predictive exploration model for MVT Pb-Zn mineralization in Central Texas : insights from the Southeast Missouri Lead District

Minor Pb-Zn occurrences on the flanks of the Llano Uplift in Central Texas have many geologic similarities to the world-class Mississippi Valley Type (MVT) deposits of Southeast Missouri. In both areas, metallic sulfides are hosted in dolomitized Cambrian carbonates, commonly in areas where local basement highs forced depositional pinch-outs of the basal sandstones. In Southeast Missouri, mineralization has been attributed to basinal fluid migration associated with late Paleozoic Ouachita deformation and appears to be spatially related to regional faults that may have served as fluid conduits. In Central Texas, there is also evidence that mineralization is associated with basinal fluids tied to Ouachita deformation and abundant regional faults serve as plausible fluid pathways. Moreover, Southeast Missouri and Central Texas are distinctly rich in Pb, an atypical characteristic for MVT deposits that are usually Zn dominant. These similarities suggest that an understanding of spatial associations between sites of known mineralization and regional geology, geochemistry, and geophysics in Southeast Missouri will be a useful guide in future exploration efforts in Central Texas. The weights-of-evidence approach is used to evaluate regional geology, geochemistry, and geophysical datasets and produce posterior probability maps for MVT deposits in Southeast Missouri. Host rock characteristics, stream sediment geochemistry, and proximity to basement highs are some of the most useful data for predicting these deposits. Model parameters derived from Southeast Missouri are applied to the Llano Uplift region of Central Texas. Although available data sets for Central Texas are less detailed than Southeast Missouri, the Central Texas models generally agree with areas known to contain MVT mineralization. A higher resolution basement structure map for Central Texas would be useful for more detailed analysis. Central Texas model results are compared with semi-quantitative geochemical data from insoluble residues from wells throughout the Llano region and indicate that the model may be useful in explaining the measured Pb concentrations in these wells. Several new permissive areas to the south and southwest of the Precambrian core of the Llano Uplift are identified that may warrant additional follow up investigation. This work illustrates the potential utility of mineral potential modeling to prioritize areas for exploration for undiscovered MVT mineralization in Central Texas.Geological Science

Handbook of Mathematical Geosciences

This Open Access handbook published at the IAMG's 50th anniversary, presents a compilation of invited path-breaking research contributions by award-winning geoscientists who have been instrumental in shaping the IAMG. It contains 45 chapters that are categorized broadly into five parts (i) theory, (ii) general applications, (iii) exploration and resource estimation, (iv) reviews, and (v) reminiscences covering related topics like mathematical geosciences, mathematical morphology, geostatistics, fractals and multifractals, spatial statistics, multipoint geostatistics, compositional data analysis, informatics, geocomputation, numerical methods, and chaos theory in the geosciences

Machine Learning Approaches for Natural Resource Data

Abstract Real life applications involving efficient management of natural resources are dependent on accurate geographical information. This information is usually obtained by manual on-site data collection, via automatic remote sensing methods, or by the mixture of the two. Natural resource management, besides accurate data collection, also requires detailed analysis of this data, which in the era of data flood can be a cumbersome process. With the rising trend in both computational power and storage capacity, together with lowering hardware prices, data-driven decision analysis has an ever greater role. In this thesis, we examine the predictability of terrain trafficability conditions and forest attributes by using a machine learning approach with geographic information system data. Quantitative measures on the prediction performance of terrain conditions using natural resource data sets are given through five distinct research areas located around Finland. Furthermore, the estimation capability of key forest attributes is inspected with a multitude of modeling and feature selection techniques. The research results provide empirical evidence on whether the used natural resource data is sufficiently accurate enough for practical applications, or if further refinement on the data is needed. The results are important especially to forest industry since even slight improvements to the natural resource data sets utilized in practice can result in high saves in terms of operation time and costs. Model evaluation is also addressed in this thesis by proposing a novel method for estimating the prediction performance of spatial models. Classical model goodness of fit measures usually rely on the assumption of independently and identically distributed data samples, a characteristic which normally is not true in the case of spatial data sets. Spatio-temporal data sets contain an intrinsic property called spatial autocorrelation, which is partly responsible for breaking these assumptions. The proposed cross validation based evaluation method provides model performance estimation where optimistic bias due to spatial autocorrelation is decreased by partitioning the data sets in a suitable way. Keywords: Open natural resource data, machine learning, model evaluationTiivistelmä Käytännön sovellukset, joihin sisältyy luonnonvarojen hallintaa ovat riippuvaisia tarkasta paikkatietoaineistosta. Tämä paikkatietoaineisto kerätään usein manuaalisesti paikan päällä, automaattisilla kaukokartoitusmenetelmillä tai kahden edellisen yhdistelmällä. Luonnonvarojen hallinta vaatii tarkan aineiston keräämisen lisäksi myös sen yksityiskohtaisen analysoinnin, joka tietotulvan aikakautena voi olla vaativa prosessi. Nousevan laskentatehon, tallennustilan sekä alenevien laitteistohintojen myötä datapohjainen päätöksenteko on yhä suuremmassa roolissa. Tämä väitöskirja tutkii maaston kuljettavuuden ja metsäpiirteiden ennustettavuutta käyttäen koneoppimismenetelmiä paikkatietoaineistojen kanssa. Maaston kuljettavuuden ennustamista mitataan kvantitatiivisesti käyttäen kaukokartoitusaineistoa viideltä eri tutkimusalueelta ympäri Suomea. Tarkastelemme lisäksi tärkeimpien metsäpiirteiden ennustettavuutta monilla eri mallintamistekniikoilla ja piirteiden valinnalla. Väitöstyön tulokset tarjoavat empiiristä todistusaineistoa siitä, onko käytetty luonnonvaraaineisto riittävän laadukas käytettäväksi käytännön sovelluksissa vai ei. Tutkimustulokset ovat tärkeitä erityisesti metsäteollisuudelle, koska pienetkin parannukset luonnonvara-aineistoihin käytännön sovelluksissa voivat johtaa suuriin säästöihin niin operaatioiden ajankäyttöön kuin kuluihin. Tässä työssä otetaan kantaa myös mallin evaluointiin esittämällä uuden menetelmän spatiaalisten mallien ennustuskyvyn estimointiin. Klassiset mallinvalintakriteerit nojaavat yleensä riippumattomien ja identtisesti jakautuneiden datanäytteiden oletukseen, joka ei useimmiten pidä paikkaansa spatiaalisilla datajoukoilla. Spatio-temporaaliset datajoukot sisältävät luontaisen ominaisuuden, jota kutsutaan spatiaaliseksi autokorrelaatioksi. Tämä ominaisuus on osittain vastuussa näiden oletusten rikkomisesta. Esitetty ristiinvalidointiin perustuva evaluointimenetelmä tarjoaa mallin ennustuskyvyn mitan, missä spatiaalisen autokorrelaation vaikutusta vähennetään jakamalla datajoukot sopivalla tavalla. Avainsanat: Avoin luonnonvara-aineisto, koneoppiminen, mallin evaluoint

Mineral exploration modeling and singularity analysis for geological feature recognition and mineral potential mapping in southeastern Yunnan mineral district, China

Nowadays, with the development in construction of geo-exploratory datasets and data processing techniques, mineral exploration modeling for recognition of mineralization associated geological features and mapping of mineral potentials become more dependent on GIS-based analysis and geo-information from multi-source datasets. Geological, geochemical and geophysical data as three main sources of geo-information in support of mineral exploration have long been employed in many researches. Spatial distributions of geological bodies or controlling factors associated with mineralization were frequently interpreted from these datasets. However, former characterizations of the controlling factors were simply focused on their location information; concerns on spatial variations of their geological signatures and controlling effects on mineralization were not sufficiently emphasized. Therefore, through a series of newly developed GIS-based manipulations, current study intends to demonstrate a comprehensive mineral exploration modeling process for more explicit recognition of controlling factors and their interactions on mineralization and delineation of hydrothermal mineral potentials in southeastern Yunnan mineral district, China. The hydrothermal mineralization as a nonlinear geo-process is accompanied with anomalous energy release and material accumulation in a narrow spatial-temporal interval. Simultaneously, it is a cascade process associated with various geological activities (e.g., magmatism, tectonism, etc.). Knowledge of these associated geo-activities is consequently beneficial to the exploration of hydrothermal mineralization. As the key point of this study, the singularity index mapping method in the context of fractal/multifractal efficient in separating geo-anomalies from both strong and weak background is applied to characterize variations of geological signatures of three controlling factors (i.e., granitic intrusions, faults and the Gejiu formation). With the guidance of multidisciplinary approaches, these geo-information derived from multi-source datasets is further integrated to produce the potential map. In comparison with traditionally used methods, the newly depicted predictor maps enhance weak geo-anomalies hidden within a strong variance of background. In addition, three geo-information integration methods including RGB composition, the principal component analysis and the weights of evidence method are implemented. By the weights of evidence method, the qualitatively and quantitatively interpretable result possessing advantages of the other two methods, simultaneously, is accepted as the final result of currently proposed mineral exploration modeling. Summarized experiences through this study will not only support future exploration in the study area, but also benefit the work in other areas

Ghana airborne geophysics project in the Volta and Keta Basin : BGS final report

This report describes the work undertaken by BGS between November 2006 and March 2009 in collaboration with Fugro Airborne Surveys Pty Ltd on an airborne geophysical survey and ground reconnaissance mapping of the Volta River and Keta Basins, Ghana. The project was supported by the EU as part of the Mining Sector Support Programme, Project Number 8ACP GH 027/13. The initial contract duration was three years, but this was extended by five months to account for acquisition of gravity data by another project. Some parts of Ghana have been airborne surveyed as part of the Mining Sector Development and Environmental Project, co-funded by the World Bank and the Nordic Development Fund, but no work was carried out on the Volta River and Keta basins, which together form a major portion of the Ghanaian territory. The approximate areas covered by the surveys are estimated at 98,000 km² for the satellite imagery and the airborne geophysics, except for the Time Domain Electromagnetic (TDEM) survey which was limited to 60,000 km². The main beneficiary of this project is the Geological Survey Department, GSD. The work enhanced its geological infrastructure and its personnel received hands-on training on modern geological mapping technology. Indirect beneficiaries were the mining and exploration companies that can follow up the reconnaissance work with detailed exploration work. The project was conducted in five phases, and this document reports on the BGS input to Phase 1, 4 and 5, with no inputs required in Phases 2 and 3: • Phase1: geological outline through Radar and optical satellite imageries. • Phase 2: airborne geophysical survey over the two basins for magnetics and Gamma Ray spectrometry (Fugro survey). • Phase 3: airborne electromagnetic and magnetic geophysical survey of specific areas, following the completion and interpretation of phase 2, using fixed wing time domain technology (Fugro survey). • Phase 4: interpretation of the combined geology and geophysics. • Phase 5: production of factual and interpretation maps. The full list of BGS products is outlined in Table 1 below, while Jordan et al. (2006) describe the products delivered on schedule in Phase 1

Advances in Computational Intelligence Applications in the Mining Industry

This book captures advancements in the applications of computational intelligence (artificial intelligence, machine learning, etc.) to problems in the mineral and mining industries. The papers present the state of the art in four broad categories: mine operations, mine planning, mine safety, and advances in the sciences, primarily in image processing applications. Authors in the book include both researchers and industry practitioners