Evidence Layers Research Articles

Translating mineral systems criteria into a prospectivity model for IOCG deposits in the Kolari region, Finland

The mineral system approach facilitates mapping the fingerprints of geological processes in ore formation using mineral prospectivity modeling (MPM). It aims to reduce the time and cost of exploration as a priority in the mineral exploration industry. Finland, which is covered with thick soil, dense vegetation, and snow in winter and thus has limited outcrops, serves as a suitable testing ground for this method. In this contribution, MPM was employed to predict favorable targets for Fe-Cu-Au (IOCG) deposits in the Kolari region, northwestern Finland. The mineral system components ore metal and sulfur sources, pathways, energy sources/drivers, traps, and the geological factors influencing the ore-forming processes were translated into mappable criteria and were considered for a regional-scale analysis. Knowledge-driven, fuzzy logic overlay, fuzzy inference system, and geometric average methods integrate evidential layers derived from geological (scale-free geology map), geochemical (till and bedrock sample geochemistry), and geophysical data (magnetic, radiometric, electromagnetic measurements, and gravity worms). Subsequently, the data were modeled using a combined conceptual/empirical approach, i.e. fuzzified evidential layers and logistic regression. In the prospectivity models, existing IOCG deposits are consistently placed within high-favorability areas. In addition, new exploration targets were identified. The models were validated against known IOCG deposits using Receiver Operating Characteristic (ROC) analysis and average Area Under the Curve (AUC) with values of final prospectivity models consistently reaching scores > 0.8. This indicates a favorable outcome of the application of MPM, making the approach applicable to other regions and ore deposit types. A majority voting ensemble technique was employed to combine the favorable areas of each prospectivity model. Then a confidence index was adopted reducing uncertainty linked to the models generated. These outputs consistently facilitated the identification of exploration targets more reliably.

Using a comparative of DRASTIC and Bayesian weights of evidence approach to assess transboundary aquifer vulnerability in a data scarcity region: Tuli-Karoo aquifer

Study regionThe Tuli-Karoo Transboundary Aquifer system is shared among Botswana, South Africa and Zimbabwe. It hosts a complex aquifer predominately of sand, heavily tapped in South Africa and sandstone substantially used in Botswana threatening it’s quality. Study focusTwo vulnerability models, the classic DRASTIC (Depth to water table [D], Recharge to the aquifer [R], Aquifer media [A], Soil media [S], Topography [T], Impact of vadose zone [I], Conductivity [C]) and the bayesian based, Weights of Evidence (WofE), were used to delineate zones vulnerable to groundwater contamination under limited nitrate observations. New hydrological insights for the regionThe study revealed that the generic DRASTIC vulnerability results were comparable to the WofE, for the highly vulnerable zones, as both had an area under the curve (AUC) below 0.6. The high vulnerability was attributed to the shallow depth of water level, fractured geological condition (based on transmissivity) and high net recharge rate in and around the Tuli-Karoo Aquifer. However, there are differences in spatial variability in the low/medium vulnerability zones. WofE was superior in capturing spatial variability of groundwater vulnerability. Considerations must therefore be provided, under data scarcity, to incorporate proxies that include landuse-landcover and population density as evidential layers. Overall, WofE performs better due to better prediction power under data-scarcity conditions. This study recommends that under data scarcity, high predictive models like WofE should be used for environmental planning and groundwater protection strategies.

Porphyry and skarn Cu prospectivity of Sumatra with GIS spatial modelling techniques

Although Sumatra Island hosts only one mid-sized, economically viable porphyry Cu deposit and a few exploited Cu-rich polymetallic skarns, studies indicate that more Cu resources remain to be discovered. This contribution integrated geochemical, structural, and relief evidential layers with the fuzzy logic- and weights-of-evidence (WoE) methods to produce mineral prospectivity maps that show a strong correlation between the generated favourable areas and the existing porphyry and skarn Cu prospects, as well as potentially new exploration districts throughout Sumatra. Moreover, a local singularity fractal map of Cu-enriched zones reduces the proposed target areas and cross-validates the results of the fuzzy model. The inclusion of relief data in the modelling filters out signal noise arising from other evidential layers and overcomes the over-fitting issue encountered in the model that excluded a relief map. The resulting Cu potential maps can thus be used as base maps for regional porphyry and skarn Cu exploration.

Improving the accuracy of detecting and ranking favorable porphyry copper prospects in the east of Sarcheshmeh copper mine region using a two-step sequential Fuzzy - Fuzzy TOPSIS integration approach

The detection and prioritization of optimal favorable areas for the ground follow-up stage are among the most challenging issues in the early stages of any mineral exploration program. A common approach to identify the favorable mineralized zones is to create and integrate independent evidential predictor layers using knowledge or data driven approaches. The method proposed in current study is not only capable of detecting favorable zones, but also provides reliable ranking of the best favorable areas to focus in the next exploration stage. For this purpose, a two-step sequential Fuzzy-Fuzzy TOPSIS approach, which deploys the merits of both Multi-Criteria Decision Making (MCDM) and Fuzzy logic inference methodologies simultaneously, is proposed. In the first step, the favorable porphyry copper mineralizations in the east of the well-known Sarcheshmeh porphyry copper mine, are detected through combining evidential layers including geological, remote sensing data, geophysical and geochemical data using fuzzy logic integration approach. As a result of the first step, a number of twenty prospects with the highest porphyry copper favorability membership were selected and inputted into the TOPSIS and fuzzy-TOPSIS algorithms. Subsequently, the chosen prospects were prioritized and ranked according to their scores acquired by each technique of the aforementioned approaches separately. The performance of each approach was evaluated thorough comparison with the known ground porphyry copper mineralizations. The results indicated the capability of the proposed approach not only in detecting favorable porphyry copper mineralization prospects consistent with the previously detected porphyry Cu mineralization but also rank them based on their priorities.

Geospatial modelling of mineral potential zones using data-driven based weighting factor and statistical index techniques

Mineral prospectivity models (MPMs) are significantly essential in delineating target zones with the optimum likelihood of containing a particular sought-after mineral deposit. This present study carried out mineral potential mapping over the Collette Prospecting Licence (PL) Area of north-western Ghana using bivariate data-driven spatial statistical models composed of statistical index (SI) and weighting factor (WF) approaches. In the first instance, the geographic coordinates of variously known locations of artisanal mining operations as well as high Au concentration locations were mapped during a field survey. As a result, 181 known locations of Au occurrences were identified, out of which 127 (70%) were selected randomly for training and creating the mineral prospectivity models, whereas the remaining 54 (30%) were used to assess and validate the accuracy of the predictive models produced. The efficacy of mineral prospectivity models generated enormously depends on the appropriate selection of mineral-related factors. In this study, the following mineral-related condition factors (evidential layers) comprising analytic signal, lineament density, uranium-thorium ratio, uranium, potassium-thorium ratio, potassium, reduction-to-equator, and geology were used. The aforementioned evidential layers were derived and sourced from geophysical and geological datasets, which were later prepared for the generation of the models in a geographic information systems (GIS) environment. Finally, the validation of the mineral prospectivity models generated was carried out by applying the receiver operating characteristics (ROC) curve. The estimated results based on the ROC plots obtained for the predictive models showed that the area under the ROC curve (AUC) scores obtained for the SI-based and WF-based mineral prospectivity models were respectively, 0.780 and 0.733. Hence, it can be concluded that both mineral predictive models created in this study produced reasonably good accuracy (AUC score greater than 0.7) in predicting the potential zones of gold mineralisation occurrences within the Collette PL Area of north-western Ghana. These MPMs can serve as essential models for mineral exploration programmes within the study area.

Application of Logistic Regression and Weights of Evidence Methods for Mapping Volcanic-Type Uranium Prospectivity

Pucheng district is a part of the Wuyi Mountain polymetallic metallogenic belt, which is constituted by Archean-Proterozoic metamorphic basements and Mesozoic volcanic-sedimentary covers. Uranium deposits are formed as volcanic-hosted and structural controls. In this study, the hybrid data-driven methods of logistic regression (LR) and weights of evidence (WofE) were applied for the mineral potential mapping of uranium in the Pucheng district. Evidential layers such as volcanic stratum, structure, igneous rock, alteration and radioactive anomaly were used in the mineral prospectivity analyses. The results show that the data-driven methods can not only measure the relative importance of each type of geological feature in uranium controls but also delineate prospective grounds for uranium exploration. The receiver operating characteristics (ROC) curve and under the ROC curve (AUC) were applied to measure the performance of the prospectivity models. The data-driven models are highly capable of mapping uranium prospectivity because AUC is close to 1. The results show that more than 90% of the known uranium deposits occur in regions with high probability. LR performs a little better than WofE in this area. The prospectivity mapping confirmed that there is significant potential for uranium mineralization for further exploration.

Mineral Prospectivity Mapping of Porphyry Copper Deposits Based on Remote Sensing Imagery and Geochemical Data in the Duolong Ore District, Tibet

Several large-scale porphyry copper deposits (PCDs) with high economic value have been excavated in the Duolong ore district, Tibet, China. However, the high altitudes and harsh conditions in this area make traditional exploration difficult. Hydrothermal alteration minerals related to PCDs with diagnostic spectral absorption features in the visible–near-infrared–shortwave-infrared ranges can be effectively identified by remote sensing imagery. Mainly based on hyperspectral imagery supplemented by multispectral imagery and geochemical element data, the Duolong ore district was selected to conduct data-driven PCD prospectivity modelling. A total of 11 known deposits and 17 evidential layers of multisource geoscience information related to Cu mineralization constitute the input datasets of the predictive models. A deep learning convolutional neural network (CNN) model was applied to mineral prospectivity mapping, and its applicability was tested by comparison to conventional machine learning models, such as support vector machine and random forest. CNN achieves the greatest classification performance with an accuracy of 0.956. This is the first trial in Duolong to conduct mineral prospectivity mapping combined with remote imagery and geochemistry based on deep learning methods. Four metallogenic prospective sites were delineated and verified through field reconnaissance, indicating that the application of deep learning-based methods in PCD prospecting proposed in this paper is feasible by utilizing geoscience big data such as remote sensing datasets and geochemical elements.

A comparative analysis of multi-index overlay and fuzzy ordered weighted averaging methods for porphyry Cu prospectivity mapping using remote sensing data: the case study of Chahargonbad area, SE of Iran

One of the specific features of porphyry copper (Cu) mineralization is the distinct occurrence of hydrothermal alteration zones, which can be mapped by processing various satellite images. Among free multispectral images, Landsat 8 OLI and ASTER data are known to be efficient in mapping different geological features, such as alteration zones and tectonic lineaments. This study aims to show the potential of these data types in mapping porphyry Cu mineralization by proposing a framework for employing and integrating different image processing methods. These methods include principal component analysis (PCA), spectral angle mapper (SAM), and matched filtering (MF) employed on these satellite images to map target al.teration zones. Moreover, PCA and directional filtering are applied to the ASTER dataset to enhance and map structural features. The results are evaluated and then combined to provide a potential map of Cu mineralization in the Chahargonbad area, located within the Urumieh-Dokhtar magmatic belt (UDMB) in Kerman province, Iran. The prediction-area plot and normalized density, which are data-driven methods, are used to assign the relative weight of each layer and evaluate them. Finally, using the calculated weights, data-driven multi-index overlay (DMIO) and fuzzy ordered weighted averaging (FOWA) methods are applied to combine the evidential layers. The potential mineralization maps created by the DMIO and FOWA provide a prediction rate of 80% and 82%, respectively. Furthermore, the accuracy of the integrated maps is investigated using the area under the curve (AUC) of the receiver operating characteristic (ROC) curves. The AUC scores obtained from the ROC curves of DMIO and FOWA methods are 0.85 and 0.88, respectively, representing powerful positive spatial relationships with mineralization areas. Based on the results, the proposed framework can be applied to provide a potential map of porphyry Cu mineralization, particularly in arid regions, with reasonable accuracy.

Identifying groundwater resilience zones in an arid inland basin using GIS-based Dempster-Shafer theory

Study regionHotan River Basin, an arid inland river basin, Northwest China. Study focusA method for assessing the spatial distribution of groundwater resilience is constructed by combining the Dempster-Shafer theory model and spatial analysis techniques. The groundwater performance indicator CRS and resilience indicator pi are calculated to present groundwater resilience. Using the Dempster-Shafer theory model to mine the data, nine groundwater conditioning factors are selected as evidential layers of the model, and the mathematical relationships between the resilience indicators and groundwater conditioning factors are established through evidence integration; Ultimately, a groundwater resilience spatial distribution map is generated, and the performance of the map is explored based on the CRS and pi values. New hydrological insights for the regionThe constructed assessment method based on this study reliably and effectively predicts the regional groundwater resilience. Validation of the groundwater resilience prediction map with pi indicates that this indicator outperforms the CRS. The areas with high groundwater resilience are mainly concentrated in the upstream oasis irrigation area and the downstream river channel. The groundwater resilience (pi) zonation map is divided into five categories: very low (Bel of 0–0.072), low (0.072–0.171), moderate (0.171–0.458), moderate high (0.458–0.786), and high (>0.786). The results can provide a scientific basis for groundwater safety and management in the Hotan River Basin.

Analysing multi-index overlay and fuzzy logic models for lode-gold prospectivity mapping in the Ahafo gold district – Southwestern Ghana

Mineral prospectivity mapping (MPM) is a multi-criteria decision-making process that identifies and prioritises potential zones for mineral exploration. Multi-index overlay and fuzzy logic models were employed in this paper to conduct GIS-based MPM of the Ahafo gold district, which is located on the northwestern margin of the Sefwi granitegreenstone belt in Ghana. Mineralisation in the area is structurally controlled with a high degree of hydrothermal alteration. Therefore, the mineral systems approach was used to translate the understanding of orogenic gold hydrothermal system into mappable exploration criteria, leading to the generation of nine evidential layers. These layers represented mappable spatial proxies for active fluid pathways (i.e. proximity to NEstriking basement faults: D2 to D3), physical traps (i.e. proximity to N- to NNE-strike sinistral fault: D4; proximity to litho-contacts; proximity to the intersection of N- to NNE- and NE-strike faults; density of N- to NNE- and NE-strike faults; litho-competency contrast), and chemical traps (i.e. proximity to Fe-rich dykes; litho-reactivity contrast; proximity to hydrothermal alteration zone), all of which are critical for hydrothermal fluid migration, focusing, and gold deposition. Weights and confidence factors were assigned to the evidential layers in the fuzzy logic approach based on experts' knowledge of how the layers are relevant to orogenic gold mineralising processes. In contrast, weights were assigned to layers in the multi-index overlay model based on prediction-area (P-A) plots and normalised densities, limiting the subjectivity of assigning weights to the layers. In order to delineate potential zones for lode-gold mineralisation in the study area, the weighted evidential layers were combined to create multi-index overlay and fuzzy prospectivity maps. The continuous prospective scores of the prospectivity maps were discretised and classified using a concentration-area (C-A) fractal model. The resulting discretised prospectivity maps based on the location of 18 known lode-gold occurrences (i.e. deposits and economic prospects) were further evaluated using P-A plots. The discretised multi-index overlay prospectivity map predicted 76% of known lode-gold occurrences, with 24% of the study area as prospective, whereas the discretised fuzzy prospectivity map predicted 74% of known lode-gold occurrences, with 26% of the study area as prospective. The high potential zones identified on the prospectivity maps were spatially located in areas with structural complexity along: (i) NE-SW structural trends (i.e. 40°–50°); and (ii) secondary structural trends in the NS to NNE-SSW direction (i.e. 0°–10° and 15°–25°). These structural trends control lode-gold mineralisation in the Ahafo gold district..

Lymphoma segmentation from 3D PET-CT images using a deep evidential network

An automatic evidential segmentation method based on Dempster-Shafer theory and deep learning is proposed to segment lymphomas from three-dimensional Positron Emission Tomography (PET) and Computed Tomography (CT) images. The architecture is composed of a deep feature-extraction module and an evidential layer. The feature extraction module uses an encoder-decoder framework to extract semantic feature vectors from 3D inputs. The evidential layer then uses prototypes in the feature space to compute a belief function at each voxel quantifying the uncertainty about the presence or absence of a lymphoma at this location. Two evidential layers are compared, based on different ways of using distances to prototypes for computing mass functions. The whole model is trained end-to-end by minimizing the Dice loss function. The proposed combination of deep feature extraction and evidential segmentation is shown to outperform the baseline UNet model as well as three other state-of-the-art models on a dataset of 173 patients.

Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (Nd) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (Nd = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (Nd = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study.

Target-scale prospectivity modeling for gold mineralization within the Rajapalot Au-Co project area in northern Fennoscandian Shield, Finland. Part 2: Application of self-organizing maps and artificial neural networks for exploration targeting

This paper is part of a two-publication series reporting target-scale prospectivity modeling results for gold enrichment in the Rajapalot project area. The study area is located in the Northern Fennoscandian Shield in Finland and consists of high-grade epigenetic-hydrothermal Au-Co prospects. The first publication of this series described the implementation of knowledge-driven- and hybrid- expert systems. In this second paper we demonstrate the application of machine learning methods such as unsupervised self-organizing maps (SOM) and K-means clustering and supervised artificial neural networks (ANNs). The results from SOM allowed the mapping of deposit-related geological patterns in the evidential layers. K-means clustering of the SOM results identified data clusters favorable for gold enrichment. Quantitative prospectivity values were computed using an ANN model trained on the SOM codebook vectors. This improved the resolution of the prospective-exploration areas mapped within the geospatial domains of the clusters, and reduced the exploration search areas. The ANN result has a significant area under curve value of 0.869 in the receiver operating characteristic plots, confirming the predictive ability of the model. Results identify new areas for exploration, as well as help prioritise drilling targets around the currently less explored prospects. In this second paper we therefore conclude that (1) SOM is an effective method for mapping and visualization of patterns (in the evidential layers) related to mineral deposits at the target scale, and (2) supervised classification of the SOM results, such as using an ANN, can help define new target areas for drilling around the existing prospects. Finally, this two-publication series collectively demonstrates that advanced machine-learning based pattern recognition and prospectivity modeling routines can be used for exploration targeting at camp- and mine- scales. The knowledge-driven methods are efficient at modeling mineralization processes and the data-driven methods are effective at identifying geological attributes related to mineral deposits and occurrences.

Groundwater potential characterisation over the Voltaian basin using geophysical, geological, hydrological and topographical datasets

The delineation of prospective zones of groundwater resources has been carried out over the Voltaian basin as a viable alternative source of potable water resource to complement increasing demand for domestic and industrial consumption, owing to the danger posed to surficial water resources within the basin by various anthropogenic activities. In achieving this objective, the groundwater potential of the area was assessed by generating a groundwater potential map (GPM) derived by integrating nine (9) evidential layers obtained from geological, geophysical, hydrological, and topographical datasets in geospatial setting using geographic information systems (GIS). The integration of these nine evidential layers was preceded by the assignment of a weight to each evidential layer based on the fuzzy analytical hierarchy process (FAHP) multi-criteria decision making (MCDM) technique. The assignment of weights to these 9 layers using the FAHP approach was done by three decision-makers (DMs) and dwelled on their relative relevance to groundwater resource delineation within the Voltaian basin. The three DMs employed in weighting these layers have expertise in applying geophysical, geological, hydrological and topographical datasets in groundwater prospecting. The weights obtained for the various evidential layers were subjected to normalisation and subsequently consolidated to produce the GPM using a fuzzy gamma operator value of 0.9 in GIS. The efficiency of the GPM was tested using the Nash-Sutclliffe efficiency (NSE) test and the Index of Agreement test. In all, 0.095%, 10.57%, 44.879%, 40.973%, and 3.974% of the study area were delineated as very high, good, moderate, poor, and very poor groundwater zones, respectively by the GPM. The NSE test yielded a value of 0.9996, and the Index of Agreement produced a value of 0.9999. In this regard, results obtained show that FAHP-based model produced is efficient and subsequently good for groundwater prediction over the Voltaian basin.

Data-driven multi-index overlay gold prospectivity mapping using geophysical and remote sensing datasets

The comparative relevance of each geospatial component of mineralization differs from one geological terrane to the other because various sought-after mineral deposit-types synonymously differ in different geological terranes. Hence, the possibility of employing a conceptual model to obtain a relationship or a quantitative function between various geospatial features (evidential layers) with respect to the mineral being sought is laudable, though these features may not necessarily have a generically related effect with the mineral being sought. As a consequence, there is the need to employ a technique that has the capacity to recognize the efficient and inefficient geospatial indicators of the mineral deposit-type being sought. In view of this, this study employed the logistic function, concentration-area fractal model and the prediction-area (P-A) plot to transform and discretize the continuous value of each evidential layer as well as generating intersection points of prediction rate indicators that are essential in obtaining the normalized densities, which were subsequently employed in generating the objective weight for each evidential layer in a data-driven way. The P-A and the normalized density techniques employed were vital in recognizing the indicator and non-indicator criteria. The results obtained acknowledged the potassium concentration layer as a non-indicator of gold mineralization within the study area and subsequently recognized the hydroxyl bearing mineral concentration layer as the most plausible indicator criteria among the six evidential layers (lineament density, iron concentration, hydroxyl concentration, gravity anomaly, magnetic anomaly and potassium concentration) employed in this study. These five indicator criteria were integrated to generate a mineral prospectivity map (MPM) over the study area based on the data-driven multi-index overlay approach adopted. The prediction rate for each of the 6 evidential layers (5 of which were the indicator criteria) as well as the MPM produced indicates that, the generation of objective weights in a data-driven manner via normalized density enhances the predicting ability of the MPM produced in comparison with the individual evidential layers.

Evidential data integration to produce porphyry Cu prospectivity map, using a combination of knowledge and data‐driven methods

ABSTRACTProducing an accurate and valid mineral prospectivity map is one of the most significant parts of mineral exploration studies. For this purpose, it is needed to obtain valid evidential layers and integrate them with an accurate methodology. Knowledge and data‐driven methods are two primary techniques applied to combine various evidential layers for mineral prospectivity mapping, of which each of them includes a variety of analytical techniques. In this study, in the first step, satellite data, aeromagnetic and airborne radiometric data, stream sediment geochemical data and geological data were applied to create valid remote sensing, geophysical, geochemical, lineaments and lithological evidential layers of the study area that are an essential factor in recognition porphyry copper mineralization, then in the second step, based on the known mineralization occurrences data, the evidential layers were weighted. Finally, these layers were integrated using fuzzy logic and index overlay methods in a combination of knowledge and data‐driven way. Validation of each layer was done using available data in the second step. The final mineral prospectivity map was evaluated, and the confirmation of this layer detected that the final mineral prospectivity map obtained from data‐driven multi‐index overlay method has a higher ore prediction rate of 76%, which identifies 24% of the area as potential zones for further exploration.

Dictionary learning for integration of evidential layers for mineral prospectivity modeling

Machine learning and deep learning anomaly detectors have been successfully used to map mineral prospectivity. However, the establishment of machine learning or deep learning anomaly detection models for mineral prospectivity mapping often requires the determination of a set of initialization parameters in the absence of ground truth data. Improper initialization parameters will degrade the performance of these mineral prospectivity mapping models. Different from most machine learning and deep learning algorithms, dictionary learning is a “white-box” algorithm involving only linear transformations, and building a dictionary learning model requires only an empirical definition of the total number of atoms. Therefore, in this paper, two dictionary learning anomaly detectors were established for mineral prospectivity modeling based on the least angle regression-Lasso (LARS-Lasso) algorithm and the iterative shrinkage-thresholding algorithm (ISTA). The following five-step procedure was proposed for mineral prospectivity modeling using the dictionary learning techniques: (a) an overcomplete dictionary is constructed based on the input data; (b) each data point is transformed into sparse coefficients according to the overcomplete dictionary; (c) the sparse representation of each data point is calculated based on the overcomplete dictionary and the sparse coefficients; and (d) the Euclidean norm of the difference between each data point and its sparse representation is calculated and used as the mineral potential of the data point. The dictionary learning models were established to model gold prospectivity in the Jinchanggouliang district, Inner Mongolia, China, and compared with the logistic regression model and one-class support vector machine model in gold exploration targeting. The result shows that (a) the performances of the dictionary learning models are no less than that of the logistic regression (LGR) model and better than that of the one-class support vector machine (OCSVM) model, and (b) the gold prospective areas differentiated by the established models are strongly consistent with geological and metallogenic characteristics in the study area. Therefore, the dictionary learning algorithms are high-performance mineral prospectivity modeling techniques. It is worth to further test the effectiveness of the dictionary learning algorithms for different types of mineral deposits in different areas in mineral exploration targeting.

Mineral prospectivity mapping using a joint singularity-based weighting method and long short-term memory network

The successful application of geographic information system (GIS)-based mineral prospectivity mapping (MPM) essentially relies on two factors: one is reasonable evidential layers that conform to geological cognition, and the other is excellent models that can extract critical prospecting information from evidential layers. Geological features in MPM are usually discretized by categorizing them into classes and assigning the same or linear weights to each class, which suffer from bias in the interpretation of geological processes under ambiguous knowledge. Moreover, either unsupervised or supervised MPM models are constructed based on the assumption that the variables are relatively independent or identically distributed. In terms of these two issues, this study develops a joint workflow that combines a rational evidence layer weighting method and a deep learning MPM model, considering both the spatial and genetic associations of geological features. A data-driven singularity-based weighting method is first applied to evaluate the relative importance of geological features for mineralization and assign continuous weights to evidential layers using a nonlinear function that is consistent with existing geological models. Then, a more recent deep learning model, namely the long short-term memory network, is employed to extract and integrate the deep-level geological prospecting information among the weighted evidence layers. This joint approach was demonstrated with the help of a case study targeting Fe mineralization in southwestern Fujian Province, China. The mineral potential map obtained using this approach revealed that almost all the known Fe mineral deposits developed in the delineated high prospective regions, indicating that the proposed workflow is reasonable and meaningful for MPM.

Ranking potentially favorable mineralization zones using fuzzy VIKOR vs. Dempster-Shafer-fuzzy AHP methods, a case study: southeast of the Sarcheshmeh copper mine, Kerman, Iran

In this study, a different ranking method has been proposed to prioritize the mineral potential areas and select the best potential area to decrease potential risks of mineral exploration. In this regard, at first, the area located in the east-southeast of the Sarcheshmeh copper mine was selected as mineral targets due to their high potential of porphyry copper occurrences. Then, the favorable porphyry copper areas were determined through collecting and integrating multiple exploratory evidential layers derived from remotely sensed imagery, geochemical, geophysical, lithological, and structural maps and images using the fuzzy logic approach in the GIS environment. Next, through building the decision matrix, the fuzzy VIKOR and Dempster-Shafer-fuzzy AHP methods were applied to estimate the favorability for copper porphyry deposits from information data layers, and the selected prospects were ranked and prioritized based on their scores obtained by each method. A comparison of the results obtained from each method with the previously discovered porphyry copper deposits and indications in the study area revealed a great match between the predicted and known deposits. The validation of results proved the ability of the proposed approach in detecting the highly favorable areas, particularly in the areas embedding known porphyry copper mineralizations. Finally, by applying the fuzzy VIKOR method to the potential areas located in the study area, the Darreh-Zar porphyry mine, as well as a region in the southeast of the Saecheshmeh mine, and a region in the south of Kouhpanj with the minimum values of Q parameter in fuzzy VOKOR method, i.e., 0.021, 0.046, and 0.166, were chosen as the best areas. The results of ranking through the Dempster-Shafer-fuzzy AHP method showed that a region in the southeast of the Sarcheshmeh mine with a priority value of 0.742, a region in the south of Kouhpanj with a priority value of 0.727, and the Dareh zar porphyry mine with a priority value of 0.653, are the best potential areas. The results of the fuzzy VIKOR and Dempster-Shafer-fuzzy AHP methods for ranking the potential areas are consistent with each other, which are validated by previously known areas as well.

Application of Knowledge-Driven Methods for Mineral Prospectivity Mapping of Polymetallic Sulfide Deposits in the Southwest Indian Ridge between 46° and 52°E

As a product of hydrothermal activity, seafloor polymetallic sulfide deposit has become the focus of marine mineral exploration due to its great prospects for mineralization potential. The mineral prospectivity mapping is a multiple process that involves weighting and integrating evidential layers to further explore the potential target areas, which can be categorized into data-driven and knowledge-driven methods. This paper describes the application of fuzzy logic and fuzzy analytic hierarchy process (AHP) models to process the data of the Southwest Indian Ocean Mid-Ridge seafloor sulfide deposit and delineate prospect areas. Nine spatial evidential layers representing the controlling factors for the formation and occurrence of polymetallic sulfide deposit were extracted to establish a prospecting prediction model. Fuzzy logic and fuzzy AHP models combine expert experience and fuzzy sets to assign weights to each layer and integrate the evidence layers to generate prospectivity map. Based on prediction-area (P-A) model, the optimal gamma operator (γ) values were determined to be 0.95 and 0.90 for fuzzy logic and fuzzy AHP to synthesize the evidence layers. The concentration-area (C-A) fractal method was used to classify different levels of metallogenic probability by determining corresponding thresholds. Finally, Receiver Operating Characteristic (ROC) curves were applied to measure the performance of the two prospectivity models. The results show that the areas under the ROC curve of the fuzzy logic and the fuzzy AHP model are 0.813 and 0.887, respectively, indicating that prediction based on knowledge-driven methods can effectively predict the metallogenic favorable area in the study area, opening the door for future exploration of seafloor polymetallic sulfide deposits.