Radial Basis Functional Link Nets Used as a Prospectivity Mapping Tool for Orogenic Gold Deposits Within the Central Lapland Greenstone Belt, Northern Fennoscandian Shield

Among the more popular spatial modeling techniques, artificial neural networks (ANN) are tools that can deal with non-linear relationships, can classify unknown data into categories by using known examples for training, and can deal with uncertainty; characteristics that provide new possibilities for data exploration. Radial basis functional link nets (RBFLN), a form of ANN, are applied to generate a series of prospectivity maps for orogenic gold deposits within the Paleoproterozoic Central Lapland Greenstone Belt, Northern Fennoscandian Shield, Finland, which is considered highly prospective yet clearly under explored. The supervised RBFLN performs better than previously applied statistical weights-of-evidence or conceptual fuzzy logic methods, and equal to logistic regression method, when applied to the same geophysical and geochemical data layers that are proxies for conceptual geological controls. By weighting the training feature vectors in terms of the size of the gold deposits, the classification of the neural network results provides an improved prediction of the distribution of the more important deposits/occurrences. Thus, ANN, more specifically RBFLN, potentially provide a better tool to other methodologies in the development of prospectivity maps for mineral deposits, hence aiding conceptual exploration.     

Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite,Nunavut, Canada

This paper outlines the process taken to create two separate gold prospectivity maps. The first was created using a combination of several knowledge-driven (KD) techniques. The second was created using a relatively new classification method called random forests (RF). The purpose of this study was to examine the results of the RF technique and to compare the results to that of the KD model. The datasets used for the creation of evidence maps for the gold prospectivity mapping include a comprehensive lake sediment geochemical dataset, interpreted geological structures (form lines), mapped and interpreted faults, lithology, topographic features (lakes), and known Au occurrences. The RF method performed well in that the gold prospectivity map created was a better predictor of the known Au occurrences than the KD gold prospectivity map. This was further validated by a fivefold repetition using a subset of the input training areas. Several advantages to the use of RF include (1) the ability to take both continuous and/or categorical data as variable inputs, (2) an internal, unbiased estimation of the mapping error (out-of-bag error) removing the need for a cross-validation of the final outputs to determine accuracy, and (3) the estimation of importance of each input variable. Efficiency of prediction curves illustrates that the RF method performs better than the KD method. The success rate is significantly higher for the RF method than for the KD method.     

Application of GIS Processing Techniques for Producing Mineral Prospectivity Maps—A Case Study: Mesothermal Au in the Swayze Greenstone Belt,Ontario, Canada

A Geographic Information System (GIS) is used to prepare and process digital geoscience data in a variety of ways for producing gold prospectivity maps of the Swayze greenstone belt, Ontario, Canada. Data used to produce these maps include geologic, geochemical, geophysical, and remotely sensed (Landsat). A number of modeling methods are used and are grouped into data-driven (weights of evidence, logistic regression) and knowledge-driven (index and Boolean overlay) methods. The weights of evidence (WofE) technique compares the spatial association of known gold prospects with various indicators (evidence maps) of gold mineralization, to derive a set of weights used to produce the final gold prospectivity map. Logistic regression derives statistical information from evidence maps over each known gold prospect and the coefficients derived from regression analysis are used to weight each evidence map. The gold prospectivity map produced from the index overlay process uses a weighting scheme that is derived from input by the geologist, whereas the Boolean method uses equally weighted binary evidence maps.The resultant gold prospectivity maps are somewhat different in this study as the data comprising the evidence maps were processed purposely differently for each modeling method. Several areas of high gold potential, some of which are coincident with known gold prospects, are evident on the gold prospectivity maps produced using all modeling methods. The majority of these occur in mafic rocks within high strain zones, which is typical of many Archean greenstone belts.     

Modeling-Based Multiscale Deep Prospectivity Mapping: A Case Study of the Haoyaoerhudong Gold Deposit,Inner Mongolia,China

The mineral system modeling approach for prospectivity mapping is an efficient and economic method to assess undiscovered mineral potential quantitatively. It is a procedure of modeling, acquiring, and coupling the proxies of footprints of mineral systems at multiple scales (e.g., regional, district, and deposit scales). In this approach, the critical issue from multiple scales is that the data collected are asymmetrical from the superficial to the deep or from mine to its brown fields, so that it is hard to employ and integrate them. To complete this study, firstly, multi-tactic 3D geological modeling methods, including the explicit, the implicit, and inversion, were used to build geological models in the condition of asymmetrical datasets at the deposit and district scales. Secondly, indicators acquired in drill-intensive fields among multisource datasets composed of geology, geochemistry, geophysics and alteration data were transferred to studies in deep and brown fields. Finally, deep (~?1,100 m) and circumjacent potentials of mine were targeted in the Haoyaoerhudong gold deposit situated in the Urad Middle Banner area, Inner Mongolia, which is one of the largest black-rock-series-type gold mines in China. This proposed procedure is more visual, clear, intuitive, and transferable to drive mineral system approach to exploration discovery than previous GIS-based studies.

     

Prospectivity Mapping for Tungsten Polymetallic Mineral Resources,Nanling Metallogenic Belt,South China: Use of Random Forest Algorithm from a Perspective of Data Imbalance

Natural Resources Research - Mineral systems are composed of many interacting components that lead to complex, singular and rare properties of geo-data. In mineral prospectivity mapping (MPM),...     

Mineral Occurrence Target Mapping: A General Iterative Strategy in Prediction Modeling for Mineral Exploration

Natural Resources Research - Procedures are discussed to construct target maps ranking the likelihood of future discoveries: for instance, of gold occurrences, knowing location and spatial context,...     

Machine Learning-Based 3D Modeling of Mineral Prospectivity Mapping in the Anqing Orefield,Eastern China

Natural Resources Research - Successful delineation of high potential targets for exploration in maturely-explored orefields is still a tough challenge. A reliable prediction model achieved by...     

Spatial Association Between Orogenic Gold Mineralization and Structures Revealed by 3D Prospectivity Modeling: A Case Study of the Xiadian Gold Deposit,Jiaodong Peninsula,China

Natural Resources Research - The Xiadian orogenic deposit with?~?100 t of gold resources, located in the Jiaodong Peninsula, Eastern China, shows an economically attractive gold...     

Logistic-Based Translation of Orogenic Gold Forming Processes into Mappable Exploration Criteria for Fuzzy Logic Mineral Exploration Targeting in the Kushaka Schist Belt,North-Central Nigeria

This paper applied a logistic-based fuzzy logic inference system to integrate critical factors that could control orogenic gold mineralization in part of the Kushaka schist belt, north-central Nigeria to develop a process-based mineral potential mapping (MPM) of the area. The critical factors from geophysical and geological dataset were weighted using logistic functions. The fuzzy logic inference system provides the capability to handle complex geological processes that culminated in orogenic gold mineralization as well as minimizing systemic uncertainties/fuzziness that often plague MPM. The results of this work show that granitic intrusions with fuzzy scores of 0.67–0.90 played a major role in generating high geothermal gradient in the area. Seventy percent of the existing gold mine sites in the area spatially coincide with metasedimentary rocks, having fuzzy scores of 0.7–0.9; this suggests metasedimentary rocks as being responsible for the production of gold fluid and ligands in the area. The evidence of hydrothermal activity, with fuzzy scores of 0.53 and 0.91, confirms the occurrence of mineralization associated with quartz veins and granite rocks. Lithological contacts and faults, having fuzzy scores of 0.60–0.80, presumably contribute to the localization of orogenic gold mineralization in the area. Emerging from the results, favorable zones for primary orogenic gold mineralization in the area occurred predominantly on granite gneiss and quartz veins. The mineral potential map was found consistent with the local geology, structural styles and hydrothermal alteration signatures in the area, and its validation using the existing locations of geochemical anomalies and prediction–area rate curve in the study area showed 75 and 72% agreement, respectively, thus confirming the reliability of the developed mineral potential map for resource management.

     

Mineral Prospectivity Mapping based on Isolation Forest and Random Forest: Implication for the Existence of Spatial Signature of Mineralization in Outliers

Natural Resources Research - Known mineralized locations and randomly chosen non-mineralized locations are used traditionally as training samples in data-driven mineral prospectivity mapping (MPM)....     

Geochemical Anomaly and Mineral Prospectivity Mapping for Vein-Type Copper Mineralization,Kuhsiah-e-Urmak Area,Iran: Application of Sequential Gaussian Simulation and Multivariate Regression Analysis

Natural Resources Research - In this paper, sequential Gaussian simulation (SGS) and number–size (N–S) fractal modeling were used for copper geochemical anomaly mapping in the western...     

Random Forest-Based Prospectivity Modelling of Greenfield Terrains Using Sparse Deposit Data: An Example from the Tanami Region,Western Australia

Data-driven prospectivity modelling of greenfields terrains is challenging because very few deposits are available and the training data are overwhelmingly dominated by non-deposit samples. This could lead to biased estimates of model parameters. In the present study involving Random Forest (RF)-based gold prospectivity modelling of the Tanami region, a greenfields terrain in Western Australia, we apply the Synthetic Minority Over-sampling Technique to modify the initial dataset and bring the deposit-to-non-deposit ratio closer to 50:50. An optimal threshold range is determined objectively using statistical measures such as the data sensitivity, specificity, kappa and per cent correctly classified. The RF regression modelling with the modified dataset of close to 50:50 sample ratio of deposit to non-deposit delineates 4.67% of the study area as high prospectivity areas as compared to only 1.06% by the original dataset, implying that the original “sparse” dataset underestimates prospectivity.     

Delineation of Sandstone Reservoir Flow Zones in the Central Bredasdorp Basin,South Africa,Using Core Samples

Natural Resources Research - This study aims to introduce a conceptual petrophysical flow zonation scheme for Valanginian age gas-bearing reservoirs in the Bredasdorp Basin, offshore of South...     

Application of Discriminant Analysis and Support Vector Machine in Mapping Gold Potential Areas for Further Drilling in the Sari-Gunay Gold Deposit,NW Iran

In this contribution, we used discriminant analysis (DA) and support vector machine (SVM) to model subsurface gold mineralization by using a combination of the surface soil geochemical anomalies and earlier bore data for further drilling at the Sari-Gunay gold deposit, NW Iran. Seventy percent of the data were used as the training data and the remaining 30 % were used as the testing data. Sum of the block grades, obtained by kriging, above the cutoff grade (0.5 g/t) was multiplied by the thickness of the blocks and used as productivity index (PI). Then, the PI variable was classified into three classes of background, medium, and high by using fractal method. Four classification functions of SVM and DA methods were calculated by the training soil geochemical data. Also, by using all the geochemical data and classification functions, the general extension of the gold mineralized zones was predicted. The mineral prediction models at the Sari-Gunay hill were used to locate high and moderate potential areas for further infill systematic and reconnaissance drilling, respectively. These models at Agh-Dagh hill and the area between Sari-Gunay and Agh-Dagh hills were used to define the moderate and high potential areas for further reconnaissance drilling. The results showed that the nu-SVM method with 73.8 % accuracy and c-SVM with 72.3 % accuracy worked better than DA methods.     

Offshore Extension of Deccan Traps in Kachchh,Central Western India: Implications for Geological Sequestration Studies

The Deccan basalts in central western India are believed to occupy large onshore–offshore area. Using geophysical and geological observations, onshore sub-surface structural information has been widely reported. On the contrary, information about offshore structural variations has been inadequate due to scarcity of marine geophysical data and lack of onshore–offshore lithological correlations. Till date, merely a few geophysical studies are reported that gauge about the offshore extent of Deccan Traps and the Mesozoic sediments (pre-Deccan). To fill this gap in knowledge, in this article, we present new geophysical evidences to demonstrate offshore continuation of the Deccan volcanics and the Mesozoic sediments. The offshore multi-channel seismic and onshore–offshore lithological correlations presented here confirm that the Mesozoic sedimentary column in this region is overlain by 0.2–1.2-km-thick basaltic cover. Two separate phases of Mesozoic sedimentation, having very distinctive physical and lithological characteristics, are observed between overlying basaltic rocks and underlying Precambrian basement. Using onshore–offshore seismic and borehole data this study provides new insight into the extent of the Deccan basalts and the sub-basalt structures. This study brings out a much clearer picture than that was hitherto available about the offshore continuation of the Deccan Traps and the Mesozoic sediments of Kachchh. Further, its implications in identifying long-term storage of anthropogenic CO₂ within sub-basalt targets are discussed. The carbon sequestration potential has been explored through the geological assessment in terms of the thickness of the strata as well as lithology.     

川中丘陵沟谷负地貌的初步研究

川中丘陵是丘陵错综起伏、沟谷纵横交错的地区。作者以这个地区的五个沟道小流域为基础,探讨了沟谷地貌的几个问题。运用数理统计的方法,揭示了川中丘陵区沟谷系的分布方向具有系统性特征;受外营力和共它随机因素影响,在分布方向上又具有随机性特征。在沟道数目、沟道比降,主沟长度和流域面积与级别之间又有着良好的几何级数关系,在结构分析的基基上,本文提出沟道上凹度、平均落差此,以及河源面积率可以作为确定沟谷系统演化阶段的定量指标。利用野外调查和形态测量资料,将川中丘陵区的沟谷划分为浅沟、切沟、冲沟、冲谷和溪谷五类,从形态数量特征揭示了沟谷发育过程的连续性和阶段性特征,并建立了沟谷演变过程的数学模型。四川盆地成为外流盆地以来,地壳运动和气候波动在该区引起了数次侵蚀旋回,产生了七期沟谷系统。沟谷和丘体的形态之间存在着高度相关,达种相关性既是沟谷、丘体地貌长期演化的结果,又是地貌形态和动力过程相互平衡的结果。运用聚类分析,认为相对高度20米、60米和100米作为划分丘陵类型的数量指标比较适宜。此外,对川中丘陵区侵蚀,搬运的基本特征以及影响流域侵蚀的诸因素作了讨论,确定了引起流域加速侵蚀的主导因素。最后,利用几个小流域的泥沙资料,建立了侵蚀模型。     

Mapping Ecotone Movements: Holocene Dynamics of the Forest Tension Zone in Central Lower Michigan,Usa

This paper presents a novel method for detecting and mapping movements of ecotone boundaries, over both time and space, using existing fossil pollen data. The method combines existing statistical techniques, including discriminant analysis and spatial interpolation, to generate statistically robust maps of ecotone boundaries over time. This method was applied to provide a comprehensive vegetative history of the forest tension zone in the Lower Peninsula of Michigan during the last 10,000 years. Pollen data for nine lakes in the Lower Peninsula were analyzed with discriminant analysis and spatially interpolated with inverse distance weighted to track ecotone movements of the forest tension zone. The forest tension zone has been in existence over the last 10,000 years and has been highly variable over space and time, with at least three major latitudinal shifts, spanning 320 km and 6° of latitude, as well as several less pronounced shifts. These shifts occurred primarily in response to millennial-scale oscillations in climate such as the warmer and dryer conditions associated with the mid-Holocene. A slighter shift was evident in association with the cooler and drier conditions of the Little Ice Age (600-100 cal yr BP). This research demonstrates the sensitivity of Great Lakes forest communities to Holocene climate change and suggests a similar sensitivity of future ecotone dynamics in response to modern global warming.     

Porphyry Molybdenum Prospectivity in the Zhongdian Arc,SW China: Use of Singularity and Factor Analyses for Resource Assessment

Natural Resources Research - The Zhongdian arc is an important mineral province in southwest China that hosts significant Cu- and Mo-rich ore deposits. These are mainly present as Late Triassic...     

Application of the Geo-Anomaly Unit Concept in Quantitative Delineation and Assessment of Gold Ore Targets in Western Shandong Uplift Terrain,Eastern China

A number of large and giant ore deposits have been discovered within the relatively small areas of lithospheric structure anomalies, including various boundary zones of tectonic plates. The regions have become the well-known intercontinental ore-forming belts, such as the circum-Pacific gold–copper, copper–molybdenum, and tungsten–tin metallogenic belts. These belts are typical geological anomalous areas. An investigation into the hydrothermal ore deposits in different regions in the former Soviet Union illustrated that the geologic structures of ore fields of almost all major commercial deposits have distinct features compared with the neighboring areas. These areas with distinct features are defined as geo-anomalies. A geo-anomaly refers to such a geologic body or a combination of bodies that their composition, texture–structure, and genesis are significantly different from those of their surroundings. A geo-anomaly unit (GU) is an area containing distinct features that can be delineated with integrated ore-forming information using computer techniques on the basis of the geo-anomaly concept. Herein, the GU concept is illustrated by a case study of delineating the gold ore targets in the western Shandong uplift terrain, eastern China. It includes: (1) analyses of gold ore-forming factors; (2) compilation of normalized regional geochemical map and extraction of geochemical anomalies; (3) compilation of gravitational and aeromagnetic tectonic skeleton map and extraction of gravitational and aeromagnetic anomalies; (4) extraction of circular and linear anomalies from remote-sensing Landsat TM images; (5) establishment of a geo-anomaly conceptual model associated with known gold mineralization; (6) establishment of gold ore-forming favorability by computing techniques; and (7) delineation and assessment of ore-forming units. The units with high favorability are suggested as ore targets.