Evidential Maps Research Articles

Mineral prospectivity modelling to delineate potential areas for gold deposits: a case study of Lupa Goldfield, South West Tanzania

ABSTRACT This study used the weight of evidence model to delineate prospective areas for gold deposits in the Lupa Goldfield. The method establishes a spatial relationship between known mineral deposits and evidential maps to create a mineral prospectivity map. Four evidential maps derived from geochemical, geological, geophysical, and topographical Digital Elevation Model (DEM) datasets were analysed and integrated to create a mineral prospectivity map. Results have revealed five classes ranked from the highest, high, medium, low, and lowest favourability patterns with probability values of 0.051, 0.0426, 0.0225, 0.0172, and 0.0091, respectively. The highest favourable areas have the best gold potentials based on the presence of predictor patterns from all four evidential maps. The posterior probability map revealed good prediction, existing gold deposits, such as Saza Mine and Shanta Gold Mine, were mapped. Results have shown that the weight of evidence model was successful and can be applied in mineral exploration targeting.

Mineral Prospectivity Mapping of Tungsten Polymetallic Deposits Using Machine Learning Algorithms and Comparison of Their Performance in the Gannan Region, China

AbstractThe current study aims at assessing the capabilities of five machine learning models in terms of mapping tungsten polymetallic prospectivity in the Gannan region, China. The five models include logistic regression (LR), support vector machine (SVM), random forest, convolutional neural network (CNN), and light gradient boosting machine (LGBM) models. Three types of data sets including geochemical data, lithostratigraphic contacts, and faults were employed to generate 16 evidential maps that were used to build the machine learning models. Tungsten polymetallic deposits were randomly separated into two parts: 80% for training and 20% for validating. Performance of the models was evaluated through receiver operating characteristic and K‐fold cross validation, with an emphasis on the variable influence within different machine learning methods. The results show that the models are especially sensitive to the chemical elements: Be, Bi, Pb, and Cd, implying that they are closely related to tungsten polymetallic mineralization. Compared to other models, the LGBM and CNN models performed best, while the LR model was the most stable. The results also indicated that the CNN model can predict the maximum number of tungsten polymetallic deposits within the minimum area based on the prediction‐area plot analysis, while the SVM model can capture the least amount of tungsten polymetallic deposits within the largest area. Finally, 18 prospective areas were delineated according to the prediction results of the machine learning models, which provided important guidance for tungsten polymetallic exploration in the study area.

Prospectivity mapping of carbonatite-associated iron oxide deposits using an integration process of ASTER and Sentinel-2A multispectral data

ABSTRACT Carbonatites and associated rocks are commonly known to host precious commodities, including rare earth elements (REE), niobium, and phosphates. They form ring-shaped complexes frequently found as small occurrences in hard-to-reach areas, making their exploration a challenging task using conventional methods. Remote sensing has been increasingly regarded as a time- and cost-saving method to map and identify new potential zones, meeting the growing need for mineral resources. In this study, we use multispectral data by combining information from ASTER and Sentinel 2A sources to delineate and map carbonatite-associated REE- and Nb-enriched iron oxide deposits. The Moroccan Oulad Dlim massif, at the NW margin of the west African craton, where carbonatite occurrences are reported, was selected as a study area. A fuzzy gamma approach was adopted to combine information from several image processing techniques, including mineral indices, principal component analysis (PCA) and minimum noise fraction (MNF). A concentration-area (C-A) fractal model is applied together with the prediction-area (P-A) plot for objective anomalies classification and prediction ability assessment for each fuzzy integrated map. Subsequently, a prospective map that predicts and defines potential areas was produced through the integration of Sentinel- and ASTER-derived evidential maps. Validation was carried out via field surveys coupled with in-situ portable X-Ray Fluorescence analysis and laboratory mineralogical characterization. The Results were consistent with field data, reaching a prediction rate of 90%. They reveal the importance of the investigated structures as potential sites for REE, Nb, and phosphates. These outcomes are encouraging and provide evidence that data fusion (ASTER-Sentinel 2A) is an effective and helpful approach for geological exploration programmes.

BWM-SAW: A new hybrid MCDM technique for modeling of chromite potential in the Birjand district, east of Iran

The Birjand chromite district, eastern Iran, is a part of the Birjand ophiolite complex. Although there is no known chromite mineralization in this district, its favorable geological setting suggests the presence of chromite endowment. Therefore, this study adopted a novel hybrid multiple-criteria decision making (MCDM) technique called BWM-SAW to mineral potential modeling (MPM) of chromite deposits in the Birjand district. For this, the authors developed three exploration evidence maps, including (1) proximity to the host rock, (2) fault density, and (3) multi-element geochemical signature. In addition, a further AHP-TOPSIS potential model was merely generated to check the adequacy of the developed BWM-SAW model. Also in this study, for the purpose of comparison and further investigation, we applied the above hybrid MCDM techniques to generate mineral potential map in the Dolatabad district hosting numerous chromite deposits. For this, we utilized three evidential maps previously generated and described by Roshanravan (2020). The results of this study manifest that the BWM-SAW technique to MPM outperformed the AHP-TOPSIS potential model and the previous continuous (i.e., data-driven index overlay, geometric average and fuzzy gamma) models of Roshanravan (2020).

The use of remotely sensed data to reveal geologic, structural, and hydrologic features and predict potential areas of water resources in arid regions

Egypt’s arid deserts resemble the Sahara. They lack water resources because of climatic conditions, although the high topographic areas receive rainfall during sporadic storms. In this article, integrating radar Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR), Landsat-8, Sentinel-2, and Shuttle Radar Topography Mission (SRTM) data reveal the geological, geomorphic, structural, and hydrological features in Wadi Batur (WB), Egypt. This defunct tributary of the Nile basin, in the eastern Egyptian desert, covers approximately 6600 km2. Analysis of the remotely sensed data shows that the northwest–southeast structures control the structures discussed in this study, which are later captured by the east–west trend. A fused ALOS/PALSAR and Landsat-8 image reveals two water outlets for the WB: one extends between the Precambrian and Phanerozoic rocks and drains in a northwestern direction to Wadi Matula in the north, and the second cuts the sedimentary rock and drains to the west, at Idfu. Ten sub-basins were extracted by using SRTM data and arranged by their infiltration properties. Eight evidential maps were combined after assigning a weight to each, and a Geographic Information Systems (GIS) technique was used to predict the groundwater recharge potential zones (GPZs). The results show that the predicted GPZs in the WB fall into six categories, ranging from excellent to bare/absent. Importantly, about 10% of the surface areas in the WB basin are characterized by an excellent recharging potential and represent the most promising zones for future groundwater extraction. Combining various datasets derived from satellite sensors is a substantial tool to detect geological features and explore groundwater resources in arid environments.

Mapping Groundwater Potential Zones Using a Knowledge-Driven Approach and GIS Analysis

Despite the Sahara being one of the most arid regions on Earth, it has experienced rainfall conditions in the past and could hold plentiful groundwater resources. Thus, groundwater is one of the most precious water resources in this region, which suffers from water shortage due to the limited rainfall caused by climatic conditions. This article will assess the knowledge-driven techniques employed to develop a model to integrate the multicriteria derived from geologic, geomorphic, structural, seismic, hydrologic, and remotely sensed data. This model was tested on the defunct Kom Ombo area of Egypt’s Nile river basin in the eastern Sahara, which covers ~28,200 km2, to reveal the promising areas of groundwater resources. To optimize the output map, we updated the model by adding the automated depression resulting from a fill-difference approach and seismic activity layers combined with other evidential maps, including slope, topography, geology, drainage density, lineament density, soil characteristics, rainfall, and morphometric characteristics, after assigning a weight for each using a Geographic Information System (GIS)-based knowledge-driven approach. The paleochannels and soil characteristics were visualized using Advanced Land Observing Satellite (ALOS)/Phased Array type L-band Synthetic Aperture Radar (PALSAR) data. Several hydromorphic characteristics, sinks/depressions, and sub-basin characteristics were extracted using Shuttle Radar Topography Mission (SRTM) data. The results revealed that the assessed groundwater potential zones (GPZs) can be arranged into five distinctive groups, depending on their probability for groundwater, namely very low (6.56%), low (22.62%), moderate (30.75%), high (29.71%), and very high (10.34%). The downstream areas and Wadi Garara have very high recharge and storage potential. Interferometry Synthetic Aperture Radar (InSAR) coherence change detection (CCD) derived from Sentinel-1 data revealed a consistency between areas with high InSAR CCD (low change) that received a plausible amount of surface water and those with very low InSAR CCD values close to 0 (high change). Landsat data validated the areas that received runoff and are of high potentiality. The twenty-nine groundwater well locations overlaid on the GPZs, to assess the predicted model, indicated that about 86.17% of the wells were matched with very good to moderate potential zones.

Identification of potential targets for kimberlite exploration using satellite imagery and map combination approach in the Lesotho Kimberlite Province

This study presents the use of satellite data and map combination approach for kimberlite exploration to identify follow-up exploration targets. The study area falls in the Lesotho Kimberlite Province, which hosts diamondiferous kimberlite pipes such as Letseng and Kao in Lesotho, and Monastery and Jagersfontein in South Africa. This study focusses on processing and integration of evidential map layers (geomorphic features, linear structures and kimberlite indicator minerals) that have known correlation with kimberlite occurrence. The evidential map layers were acquired from satellite platforms such as ASTER and SRTM DEM. These layers were integrated to identify favourable sites for detailed follow-up exploration by subdividing the area into high, moderate and low prospective regions based on the number of controlling evidences operating in each sub-region. A follow-up sampling and ground magnetic survey were carried out at one of the high prospective zones to validate the results of the present approach. Kimberlite indicator minerals such as garnet, diopside, ilmenite and chromite were recovered from the collected sample. Several garnets show adhering materials known as kelyphite rims, which are typical surface features of peridotitic garnets having close relationship with diamondiferous kimberlite. Keating correlation coefficient, which uses a simple pattern recognition to locate magnetic anomalies that resemble the response of modelled kimberlite pipes, was applied to ground magnetic data. We used a series of reference kimberlite models composed of homogeneous vertical cylinders of various radius ranging from 50 to 200 m and depth varying from 50 to 500 m. The correlation coefficients between analytical signal grid of modelled kimberlite pipe and observed magnetic data were computed. Following this approach, kimberlite model with correlation coefficients exceeding 80% was selected. By matching the response of modelled kimberlite pipe with the magnetic anomalies, four pipe-like targets were located. The target(s) may be a potential source of the recovered kimberlite indicator minerals since the morphology of these minerals suggest a proximal kimberlitic source.

Improved index overlay mineral potential modeling in brown- and green-fields exploration using geochemical, geological and remote sensing data

The data-driven index overlay technique is an amended version of the conventional index overlay method, which has been utilized for mineral potential modeling (MPM) in brown-fields exploration, whereby a data-driven way is utilized to determine the relative significance of both individual evidence maps and evidential values. Although this method evades exploration bias in the conventional index overlay MPM resulting from categorization of evidential maps into some optional classes, it still possesses two major issues: (i) since this technique utilizes the plots of prediction-area (P-A) for defining the importance degree of every evidential map, it carries exploration bias resulting from the use of known mineral occurrences (KMOs) and (ii) it is not applicable in green-fields areas where there may be no KMOs. In this study, we propound an improved index overlay MPM to overcome the deficiencies described above, whereby every individual evidential map is rendered a weight without expert opinion and without the use of KMOs through a modified Shannon’s entropy method. To illustrate this procedure, we applied it to the brown-fields Dolatabad district of southern Iran as a MPM case study for podiform chromite deposits. The results indicated that the improved index overlay approach outperformed the data-driven index overlay method for modeling the potential of the podiform chromite deposits. After successful testing of the improved index overlay technique, we applied it to the green-fields Birjand district of eastern Iran in order to ascertain promising zones for further detailed exploration of the targeted deposit.

Weighted Photolineaments Factor (WPF): An Enhanced Method to Generate a Predictive Structural Evidential Map with Low Uncertainty, a Case Study in Chahargonbad Area, Iran

Decreasing the uncertainty and increasing the prediction rate of evidential maps are two important objectives in mineral prospectivity mapping (MPM). In this paper, we proposed weighted photolineaments factor (WPF) to generate a continuous weighted structural evidential map with high prediction rate and low uncertainty in Chahrgonbad area. The WPF which has four components was defined as weighted sum of the values related to four following characteristics of lineaments: (a) number, (b) length, (c) number of intersection and (d) number of directional sets. We used the extracted lineaments from geological map and processing of aeromagnetic data in our analysis to enhance the prediction rate of WPF map. The process was implemented using an effective multistage algorithm including: texture analysis, phase symmetry analysis, non-maximal suppression, thresholding, skeletonization and line fitting process. Afterward, values related to various characteristics of lineaments were extracted and transformed by a logistic function to improve their predictive ability. Then, using the concentration-area fractal model and prediction-area plot, the prediction rate of WPF components was derived and their importance weights were calculated using normalized density. Finally, the WPF map was generated and evaluated using location of 18 known mineral occurrences in the study area. The results demonstrated a good concordance with our previous geological knowledge and experience about Cu mineralization in Chahrgonbad area. The number of lineaments directional sets and the number of lineaments were recognized as structural criteria with the highest prediction rate for Cu prospectivity. The generated WPF map has a higher prediction rate (80%) in comparison with each individual component and delineated 20% of the study area as prospective target. Eventually, three zones with high structural permeability which are fertile environment for emplacement of intrusions, circulation of magmatic-hydrothermal fluids and Cu mineralization were delineated on the WPF map and proposed for further exploration.

A Study on Training Data Selection for Object Detection in Nighttime Traffic Scenes

Grid mapping is widely used to represent the environment surrounding a car or a robot for autonomous navigation. This paper describes an algorithm for evidential occupancy grid (OG) mapping that fuses measurements from different sensors, based on the Dempster-Shafer theory, and is intended for scenes with stationary and moving (dynamic) objects. Conventional OGmapping algorithms tend to struggle in the presence of moving objects because they do not explicitly distinguish between moving and stationary objects. In contrast, evidential OG mapping allows for dynamic and ambiguous states (e.g. a LIDAR measurement: cannot differentiate between moving and stationary objects) that are more aligned with measurements made by sensors. In this paper, we present a framework for fusing measurements as they are received from disparate sensors (e.g. radar, camera and LIDAR) using evidential grid mapping. With this approach, we can form a live map of the environment, and also alleviate the problem of having to synchronize sensors in time. We also designed a new inverse sensor model for radar that allows us to extract more information from object level measurements, by incorporating knowledge of the sensor’s characteristics. We have implemented our algorithm in the OpenVX framework to enable seamless integration into embedded platforms. Test results show compelling performance especially in the presence of moving objects.

Multi-Sensor Fusion in Dynamic Environment using Evidential Grid Mapping

The performance of autonomous agents in both commercial and consumer applications increases along with their situational awareness. Tasks such as obstacle avoidance, agent to agent interaction, and path planning are directly dependent upon their ability to convert sensor readings into scene understanding. Central to this is the ability to detect and recognize objects. Many object detection methodologies operate on a single modality such as vision or LiDAR. Camera-based object detection models benefit from an abundance of feature-rich information for classifying different types of objects. LiDAR-based object detection models use sparse point clouds, where each point contains accurate 3D position of object surfaces. Camera-based methods lack accurate object to lens distance measurements, while LiDAR-based methods lack dense feature-rich details. By utilizing information from both camera and LiDAR sensors, advanced object detection and identification is possible. In this work, we introduce a deep learning framework for fusing these modalities and produce a robust real-time 3D bounding box object detection network. We demonstrate qualitative and quantitative analysis of the proposed fusion model on the popular KITTI dataset.

Application of remote sensing and GIS techniques for exploring potential areas of hydrothermal mineralization in the central Eastern Desert of Egypt

The integration of remotely sensed data allowed the successful characterization of the hydrothermal alteration zones of the Abu–Gaharish area in the Central Eastern Desert of Egypt using image transformation techniques. The integration of several evidential maps highlighted the plausible areas with high concentrations of argillic and phyllic, key hydrothermal minerals that reflect the intensity of hydrothermal effects and the probable sites of ore bodies. The excellent prospective zone identified in the map occupies around 1.39% of the study area. Kaolinite–smectite minerals with correlation scores of around 0.97 were consistent with areas of hydrothermal alteration and mining activities; however, propylitic minerals were confined to basic metavolcanics and serpentinites. The resulting predictive map was assessed using existing mining data with significant consistency; therefore, field verification is essential. The overall results showed that remotely sensed data represent a valuable mapping tool for geological and mineral resources reconnaissance in the arid regions

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

Mineral systems are composed of many interacting components that lead to complex, singular and rare properties of geo-data. In mineral prospectivity mapping (MPM), supervised machine learning algorithms, which have advantages in dealing with complex geo-data, usually involve uncertainty resulting from the discretization of continuous evidential maps into arbitrary classes as well as the large data imbalance caused by the rarity of deposit locations. Consequently, the predicted results may be biased. In this paper, a random forest (RF) algorithm based on the bagging technique is used to map the prospectivity of tungsten polymetallic deposits in the Nanling metallogenic belt. Data-driven logistic transformation is employed to obtain continuous evidential maps. Both discretized and continuous evidential maps are used to generate prospectivity models for comparison. To reduce the data imbalance, the under-sampling method and the synthetic minority over-sampling technique (SMOTE) are implemented to generate balanced datasets. The receiver operating characteristic (ROC) curve and improved prediction-area (P-A) plot are applied to evaluate the prospectivity models. The predictive results show that when using the RF algorithm in MPM, the application of continuous evidential maps can improve the performance of prospectivity models and reduce the uncertainty resulting from the discretization of evidential maps. The prospectivity model trained with a balanced SMOTE-generated dataset shows the best overall performance for improving the percentage of deposit locations that are correctly predicted and decreasing the percentage of non-deposit locations that are inaccurately identified as deposit locations to some extent. In addition, the improved P-A plot is superior to the ROC curve because the latter neglects the occupied area, which is critical for mineral exploration and may provide an overly optimistic performance with imbalanced data. However, further testing of the evaluation criteria and the SMOTE approach to reduce data imbalance is warranted to determine fully the universality in MPM from the perspective of data imbalance. Based on prospectivity models, four high-potential areas and five moderate-potential areas are delineated, which indicates good future prospecting for tungsten polymetallic deposits in this region.

BWM-ARAS: A new hybrid MCDM method for Cu prospectivity mapping in the Abhar area, NW Iran

Mineral prospectivity mapping (MPM) is reckoned as a lucrative tool regarding exploration programs that include lengthy and multi-branched decision pathways with variously conceivable outcomes. This paper outlines the prosperous application of a new hybrid multi-criteria decision-making (MCDM) method called BWM-ARAS for integrating multisource geological dataset to delineate highly Cu prospectivity areas in the Abhar area, NW Iran. In fact, the BWM-ARAS method is a compound of two distinct procedures, in which the BWM method is exploited for weighting to criteria and sub-criteria while ARAS method is responsible for ranking the varieties of alternatives. Herein, BWM-ARAS method is implemented on a set of multisource geoscience data as targeting criteria (derived from geological, geochemical, and remote sensing dataset) pertaining to the mineral deposit of type sought that are termed evidential maps. For fulfilling the integration process, the spatial values of evidential maps are initially transformed to a new space ranging [0, 1] by the logistic sigmoid function, and then discretized through C-A fractal-based model to ascertain thresholds corresponding favorable populations. In addition, a fuzzy MPM, a straightforward knowledge-based method, is accomplished to peruse the adequacy of the former method. The predication-area (P-A)plot and normalized density (Nd) as authoritative methods were conducted to evaluate the efficiency of abovementioned prospectivity models. Accordingly, the obtained results corroborated the higher authenticity of the BWM-ARAS model (Nd=7.33) compared with the fuzzy model (Nd=3.54) in delimiting the sought areas.

Application of fuzzy logic and geometric average: A Cu sulfide deposits potential mapping case study from Kapsan Basin, DPR Korea

In this paper, two kinds of knowledge-driven methods, one using the fuzzy logic and another using geometric average, were applied to create the mineral potential maps for Cu sulfide deposits in the greenfield Kapsan Basin, DPR Korea. The ore geology studies for the study area have revealed that Cu sulfide deposits of hydrothermal genesis in Kapsan Basin are closely associated with Jurassic intrusions and faulting tectonics. Based on the conceptual model of Cu sulfide deposits and the available spatial datasets in the study area, we used five independent evidential maps for Cu sulfide deposits potential mapping. They include: (1) faults; (2) aeromagnetic anomaly; (3) Cu geochemical data; (4) Pb geochemical data; and (5) Zn geochemical data. The evidential map values were transformed into continuous values of the [0, 1] range using the non-linear fuzzy membership functions; logistic sigmoid and fuzzy Gaussian functions. Because the fuzzy logic and geometric average methods can use the same fuzzification methodology based on suitable membership functions, it is very economic and efficient to simultaneously apply two predictive models for mineral potential mapping of the study area. The preparation of these evidential layers were performed using spatial analyses supported in ArcGIS 10.4 GIS platform based on geological, geophysical and geochemical spatial datasets.The validation and comparative analysis results for the two predictive models demonstrated that most of known mineral occurrences are distributed in areas with high potential values. The target areas classified by the fuzzy logic occupy 15% of the study area and contain 78% of the total number of known mineral occurrences. Compared with the fuzzy logic, the resulting areas by the geometric average occupy 13% of the study area, but contain 93% of the total number of known mineral occurrences. Although the total number of known mineral occurrences is relatively low for the application of ROC (receiver operating characteristics) technique, the areas under the ROC curve (AUC) obtained by two predictive models were greater than 0.5, suggesting that both predictive models and their resulting potential maps are useful for evaluating the prospectivity of Cu sulfide deposits in Kapsan Basin.

Maximum Entropy and Random Forest Modeling of Mineral Potential: Analysis of Gold Prospectivity in the Hezuo–Meiwu District, West Qinling Orogen, China

This study tested and compared the mineral potential mapping capabilities of the random forest (RF) and maximum entropy (MaxEnt) algorithms using gold deposit occurrences within the Hezuo–Meiwu district, West Qinling Orogen, China. Eighteen orogenic gold deposits in this district and associated regional exploration datasets were used to construct data-driven predictive models to identify locations prospective for gold mineralization. The 18 orogenic gold deposits used in the modeling can be divided into magmatic-hydrothermal gold deposits and mesothermal gold deposits in terms of metallogenic characteristics and nine evidential maps associated with Au deposit occurrences (i.e., distance to intrusions and faults; Au, As, Ag, Cu, and Sb singularity indices; and principal component scores (PC1 and PC2) based on isometric logratio-transformed geochemical data were selected as inputs to the models). The PC1 represents a primary geochemical signature of tectonic process or their products (i.e., fault system), whereas PC2 represents a secondary geochemical signature. Both RF and MaxEnt models were then used to quantitatively rank the importance and identify the sensitivity of the evidential maps based on their spatial relationships to the known gold deposits in the study area. The two groups of populations in the response curves and marginal effect curves indicate that the mineral potential mapping should be performed by zones in consideration of different metallogenic characteristics of gold deposits. The accuracy of the resulting models was then assessed, and the results of the mineral potential mapping were examined using receiver operating characteristic (ROC) analysis, capture-efficiency curve, and success rate curve. Both mineral potential mapping by zones with RF and MaxEnt models have higher area under the ROC curve (AUC) values than the models performed in the study area and delineate 19% of the study area containing > 88% of the known deposit occurrences. Finally, according to the concentration–area (C-A) thresholds for prospectivity maps, two ternary prospectivity maps were generated for further mineral exploration. The results indicate that the RF and MaxEnt algorithms can be used effectively for mineral potential mapping and represent machine learning algorithms that can be used in areas with a few known mineral occurrences.

A comparison between knowledge-driven fuzzy and data-driven artificial neural network approaches for prospecting porphyry Cu mineralization; a case study of Shahr-e-Babak area, Kerman Province, SE Iran

The study area, located in the southern section of the Central Iranian volcano–sedimentary complex, contains a large number of mineral deposits and occurrences which is currently facing a shortage of resources. Therefore, the prospecting potential areas in the deeper and peripheral spaces has become a high priority in this region. Different direct and indirect methods try to predict promising areas for future explorations, most of which are very time-consuming and costly. The main goal of mineral prospecting is applying a transparent and robust approach for identifying high potential areas to be explored further in the future. This work presents the procedure taken to create two different Cu-mineralization prospectivity maps. The first map is created using a knowledge-driven fuzzy technique and the second one by a data-driven Artificial Neural Network (ANN) approach. In this study aim is to investigate the results of applying the ANN technique and to compare them with the outputs of applying the fuzzy logic method. The geo-datasets employed for creating evidential maps of porphyry Cu mineralization include the solid geology map, alteration map, faults, dykes, airborne total magnetic intensity, airborne gamma-ray spectrometry data (U, Th, K and total count), and known Cu occurrences. Based on this study, the ANN technique is a better predictor of Cu mineralization compared to the fuzzy logic method. The ANN technique, due to capabilities such as classification, pattern matching, optimization, and prediction, is useful in identifying the anomalies associated with the Cu mineralization.

Grid-Based Environment Estimation Using Evidential Mapping and Particle Tracking

Modeling and estimating the current local environment by processing sensor measurement data is essential for intelligent vehicles. Static obstacles, dynamic objects, and free space have to be appropriately represented, classified, and filtered. Occupancy grids, known for mapping static environments, provide a common low-level representation using occupancy probabilities with an implicit data association through the discrete grid structure. Extending this idea toward dynamic environments with moving objects requires a static/dynamic classification of measured occupancy and a tracking of the dynamic state of grid cells. In this paper, we propose a new dynamic grid mapping approach. An evidential representation using the Dempster-Shafer framework is used to model hypotheses for static occupancy, dynamic occupancy, free space, and their combined hypotheses. These hypotheses are consistently estimated and accumulated in a dynamic grid map by an adapted evidential filtering, allowing one to distinguish static and dynamic occupancy. The evidential grid mapping is combined with a low-level particle filter tracking that is used to estimate cell velocity distributions and predict dynamic occupancy of the grid map. Static occupancy is directly modeled in the grid map without requiring particles, increasing efficiency, and improving the static/dynamic classification due to the persistent map accumulation. Experimental results with real sensor data show the effectiveness of the proposed approach in challenging scenarios with occlusions and dense traffic.

Knowledge-Driven Prospectivity Mapping for Granite-Related Polymetallic Sn–F–(REE) mineralization, Bushveld Igneous Complex, South Africa

This paper presents mineral prospectivity mapping to identify potential new exploration ground for polymetallic Sn–F–REE mineralization associated with the Bushveld granites of the Bushveld Igneous Complex, South Africa. The Lebowa Granite Suite, commonly known as the Bushveld granites, is host to a continuum of polymetallic mineralization with a wide range of metal assemblages (Sn–Mo–W–Cu–Pb–Zn–As–Au–Ag–Fe–F–U–REE), ranging from a high-temperature to a low-temperature magmatic hydrothermal mineralizing environment. The prospectivity map was generated by fuzzy logic modeling and a selection of targeting criteria (or spatial proxies) based on a conceptual mineral system highlighting critical processes responsible for the formation of the polymetallic mineralization. The spatial proxies include proximity to differentiated granites (as heat and metal-rich fluid sources), Rb geochemical map (fluid-focusing mechanism such as fractionation process), principal component maps (PC 4 Y–Th and PC 14 Sn–W, fluid pathways for both high- and low-temperature mineralization) and proximity to roof rocks (traps for fluids). Logarithmic functions were used to rescale rasterized evidential maps into continuous fuzzy membership scores in a range of [0, 1]. The evidential maps were combined in two-staged integration matrix using fuzzy AND, OR and gamma operators to produce the granite-related polymetallic Sn–F–(REE) prospectivity map. The conceptual mineral system model and corresponding prospectivity model developed in this study yielded an encouraging result by delineating the known mineral deposits and occurrences of Sn–F–(REE) mineralization that were not used to assign weights to the evidential maps. The prospectivity model predicted, on average, 77% of the known mineral occurrences in the BIC (i.e., 56 of 73 Sn occurrences, 12 of 15 F occurrences and 6 of 8 REE occurrences). Based on this validation, 13 new targets were outlined in this study.

Calcic iron skarn prospectivity mapping based on fuzzy AHP method, a case Study in Varan area, Markazi province, Iran

Fuzzy analytical hierarchy (AHP) approach is a method for mineral prospectivity mapping (MPM) that is generally used for mineral exploration. This method is feasible for multi-criteria decision-making (MCDM) issues. Geographical information system (GIS) and fuzzy AHP have been applied in this paper to obtain prospectivity model for Calcic Iron Skarn (CIS) mineralization. Several thematic (such as geological, geophysical, and geochemical) geo-datasets have been collected, analysed and integrated based on fuzzy AHP method, in Varan area, central of Iran. Three professional economic geologists with the experience on exploration of CIS mineralization have been used to allocate appropriate weights to layers. Then fuzzy operator was used to integrate weighted evidence layers and mineral prospectivity map of CIS mineralization in Varan area was generated. Eventually for confirming the accuracy of the applied manner, locations of recognized mineral deposits in the Varan area were compared with the generated mineral prospectivity map. The results presented acceptable responses. For detailed assessment of the CIS mineralization model and due to the fact that geological features have fractal dimensions, we used C-A model for classification of prospectivity model to determine thresholds for the final prospectivity map. C-A fractal model, determine thresholds for classifying values in evidential maps. The final prospectivity map was confirmed by checking field of three target areas.