Search

An integrated interpretation of processed 3D seismic data, a suite of well logs, biostratigraphic data and core photographs of AMO Field, deep offshore Niger Delta is carried out for the sequence stratigraphic analysis of strata penetrated by wells. Lithologic and key stratigraphic surfaces, that is the maximum flooding surfaces (MFSs) and sequence boundaries (SBs), are identified based on bioevents and log motifs. Biostratigraphic data help the delineation of MFSs and SBs, as well as dating, determination of depositional environment and paleobathymetry. The sequence stratigraphic study reveals seven MFSs (of 13.0 Ma, 11.0 Ma, 9.5 Ma, 7.4 Ma, 5.8 Ma, 5.0 Ma and 2.7 Ma) and six SBs (of 12.1 Ma, 10.3 Ma, 8.5 Ma, 6.3 Ma, 5.5 Ma and 4.2 Ma). The key stratigraphic surfaces identified involve three depositional sequences characterized by lowstand, transgressive and highstand system tracts respectively. The studied section is deposited in a deepwater environment ranging from Lower to Upper Bathyal and can be dated back to the Middle Miocene - Middle Pliocene in age. Seven faults and two horizons (corresponding to reservoir units) are identified and mapped across the seismic data volume to establish a continuity of the reservoir to areas lacking well control. The established sequence stratigraphic framework displays successions of sediments (system tracts) where the reservoir sand units of the lowstand and highstand system tracts are of hydrocarbon prospective units in the field.

Magnetite-bearing granophyre and quartz dolerite are the evolved fractions of differentiated dolerite (diabase) sills and are an important host to Archean gold deposits because they are chemical traps for orogenic fluids. Despite their economic importance, there is a poor understanding of how melt composition, crystal fractionation, sill geometry, and depth of emplacement increase the volume of host rock that is most favorable for gold precipitation during orogenesis. We use drill core logging, whole-rock geochemistry, magnetic susceptibility, gold assay, and thermodynamic modeling data from 11 mineralized and unmineralized ca. 2.7 Ga differentiated dolerites in the Eastern Goldfields superterrane (Yilgarn craton, Western Australia) to better understand the influence of igneous and emplacement processes on gold prospectivity. Orogenic gold favors differentiated dolerites, derived from iron-rich parental magmas, that crystallize large volumes of magnetite-bearing quartz dolerite (>25% total thickness). Mineralized sills are commonly >150 m thick and hosted by thick and broadly coeval sedimentary sequences. Sill thickness is an important predictor for gold prospectivity, as it largely controls cooling rate and hence fractionation. The parental melts of gold-mineralized sills fractionated large amounts of clinopyroxene and plagioclase (possibly up to 50%) at depth before emplacement in the shallow crust. A second fractionation event at shallow levels (

There has been in recent years a trend towards adopting deep neural networks for addressing earth science problems. Of the various deep neural networks applied to different problems in earth sciences, this study aimed to demonstrate how to apply the group method of data handling (GMDH) neural networks for mineral prospectivity mapping (MPM). GMDH neural networks are sophisticated, multilayered, robust tools for addressing complex regression problems. However, labeled data for MPM, which constitute multivariate attributes of known deposit and non-deposit sites, are often (if not always) insufficient to train neural networks adequately. Such issue triggers networks' poor generalization; that is, the networks developed fit the labeled data perfectly (i.e., low bias) but cannot predict unseen data accurately (i.e., high variance). Given this, GMDH neural networks were, in this study, coupled with a window-based data augmentation technique, an approach to generate additional geologically constrained labeled samples for MPM, and applied to a district hosting few but giant porphyry copper deposits. It was recognized that coupling the data augmentation technique with GMDH neural networks yielded robust predictive models that can handle the bias–variance tradeoff, making this combined methodology a viable option for MPM in terrains that host few but large mineral deposits.

The application of deep learning algorithms in mineral prospectivity mapping (MPM) is a hot topic in mineral exploration. However, few studies have focused on recurrent neural networks (RNNs) in terms of integrating different evidential layers to map mineral potential. In this study, a gated recurrent unit (GRU) model was employed for MPM using a case study on the Baguio District of Philippines. To generate sufficient training samples for GRU, data augmentation with geological constraints was employed. To explore the influence of different orders of evidence layers, as inputs of RNN, a total permutation of the evidence layers was built. Meanwhile, a nonlinear controlling function was used to capture the spatial relationships between known mineral deposits and geological controlling factors and assign the value for each pixel of evidence layers. The obtained results demonstrated the excellent performance of GRU in MPM. The delineated high-anomaly areas show close spatial relationships with known mineral deposits and therefore can provide significant clues for the next round of mineral exploration in the study area.

The Sonakhan greenstone belt in Central India is under-explored with respect to gold in spite of its similarity to auriferous greenstone belts in general, which prompted a prospectivity analysis. The workflow involved adoption of a conceptual mineral systems model, recognizing indicative spatial proxies, processing exploration datasets, generating evidence maps and integrating into GIS-based mineral prospectivity mapping. Available geological information such as key lithologic units and their contacts was combined with geochemical anomalies of selected pathfinder elements, geophysical data (aeromagnetic anomaly and K/Th ratio map) and satellite digital image data (ASTER and Landsat 7 ETM +), leading to generation of 17 evidential layers. The lack of a significant number of known mineral occurrences in the study area precludes the use of data-driven prospectivity modeling techniques. Therefore, knowledge-driven approaches such as binary and multiclass index overlay, fuzzy logic and fuzzy AHP (analytic hierarchy process) were adopted to integrate the evidential layers resulting in four prospectivity maps. The variation of cumulative prospectivity with respect to cumulative area in each model was used to determine threshold to produce binary prospectivity maps separating high and low prospectivity zones. An approach based on the unique conditions of the binary prospectivity maps was used to illustrate the combined results of different models. In order to quantify the intuitive uncertainty in exploration targeting that arose due to different model outputs, a modulated predictive model was generated taking the mean prospectivity values at each pixel. The pixels having mean values above 95th percentile were grouped and the area delineated as potential exploration targets for gold that comprises merely 5% of the study area. The estimated uncertainty and confidence values for each pixel were used in the risk analysis that returned 1.95% and 3.05% of the study area as low- and high-risk exploration targets, respectively.

To integrate the evidential signatures into a mineral prospective map, it is necessary to model the observed data of metallogenic predictors with complex relationships. In this paper, a methodology was developed for gold prospectivity modeling by combining Laplacian eigenmaps (LEMS) and the least angle regression (LARS). LEMS were used to transform metallogenic predictors into Laplacian spectra to enhance the data classification ability, and the Laplacian spectra were used in LARS for gold prospectivity modeling. The Jinchanggouliang area, an important gold metallogenic district in China, was taken as the study area. Based on the geological and geochemical survey data of the study area, the LARS model, the combination of LEMS and LARS (LEMS–LARS) model, the logistic regression (LGR) model and the one-class support vector machine (OCSVM) model were each established for gold prospectivity modeling in the study area. The performances of these four models were measured using different performance evaluation methods. The receiver operating characteristic (ROC) analysis reveals that the LEMS–LARS model, the LGR model and the OCSVM model yield comparable performances and were superior to the LARS model, for gold prospectivity modeling in the study area. The prediction–area (P–A) plots of the four models show that the LEMS–LARS model, the LGR model and the OCSVM model each predicted about 20% of the study area as gold prospective areas, which contain about 80% of the known gold deposit locations. However, the performance of the LARS model was difficult to evaluate by the P-A plot because its prediction curve overlaps with its area curve, forming a straight-line segment, in the P-A plot. The gold prospective areas delineated optimally separately by the LEMS–LARS model, the LGR model and the OCSVM model are associated spatially with areas having favorable metallogenic conditions. The case study illustrates that the LEMS–LARS model can be used as a data-driven mineral prospectivity method, and its performance, in gold prospectivity modeling in the study area, is comparable to that of the LGR method and OCSVM method.

The Xiadian orogenic deposit with ~ 100 t of gold resources, located in the Jiaodong Peninsula, Eastern China, shows an economically attractive gold mineralization that is spatially associated with the Zhaoping detachment fault. This study presents a combination of field investigation, 3D modeling, spatial analysis of models, and prospectivity modeling using a multi-layer perceptron deep neural network to understand the association between gold mineralization and structural deformation and to identify targets for deep exploration. The principal gold mineralization at Xiadian occurs as disseminated ore associated with strongly fractured rocks. Spatial analysis determined that gold mineralization is located primarily in convex segments (0–50 m) of the fault footwall within 150 m of the fault buffer where the dip is gentle (~ 35° to 55°). Sulfide vein ore with high Au grades is, by contrast, typically sited within steeply dipping fracture-fill distal to the Zhaoping detachment (150–250 m), consistently in areas with NE-strike and associated with steep segments of the detachment fault. The distribution of the orebodies is attributed to brittle–ductile deformation proximal to the detachment and distal brittle extension. Given the characteristics of syn-deformation mineralization and the stress regime (NW σ3, NE σ2, and vertical σ1), the NE-plunging ore shoots were interpreted as the result of auriferous fluid flow into oblique dilation zones formed by the coupled normal and dextral strike-slip movement of the Zhaoping fault. Six potential gold targets were identified sub-horizontal to, and NE-trending from, known orebodies. This study highlights the utility of 3D prospectivity modeling as a robust tool for understanding orogenic gold spatial distribution and related structural controls.

The prospecting cost–benefit strategy is an easy-to-implement mineral prospectivity modeling algorithm which uses the likelihood ratio, lift index and Youden index to represent the mineral potential. In this study, the prospecting cost–benefit strategy was extended to use the Matthews correlation coefficient (MCC) and F-measure to represent the mineral potential, and compared with the bagging and boosting ensembles of decision stumps in polymetallic prospectivity modeling in the Lalingzaohuo district (Qinghai Province, China). Replacing the decision trees with decision stumps in the bagging algorithm can alleviate the overfitting problem of the bagging ensemble model caused by the depth of the decision trees. According to the polymetallic prospectivity modeling results of the extended prospecting cost–benefit strategy, and bagging and boosting ensembles, seven mineral potential maps were produced, including likelihood ratio map, lift index map, Youden index map, MCC map, F-measure map, classification score maps. The receiver operating characteristic (ROC) curves show that the prospecting cost–benefit strategy is superior to the ensemble learning models in polymetallic prospectivity modeling. According to the seven mineral potential maps, polymetallic prospective areas were optimally delineated in the study area. These polymetallic prospective areas account for only a small percentage of the whole study area (12.61 – 16.95%) but contain almost all known polymetallic deposits (94 – 100%). Therefore, the MCC and F-measure can be used to represent the mineral potential in the prospecting cost–benefit strategy. The extended prospecting cost–benefit strategy performs better than the ensemble learning algorithms in polymetallic prospectivity modeling.

While performing various steps for exploration and exploitation of geothermal resources, the applications of geochemical methods are pivotal for reservoir characteristics evaluation. It is used concurrently with geological and hydro-geological appraisals to supplement available information regarding the prospectively of the region of investigation at the relatively low cost compared to geophysical methods or drilling. Temperature range of 40 °C to 47 °C is demonstrated by the study area comprising of several thermal water manifestations. Studies from existing wells and surveys have suggested the presence of a sizable low enthalpy geothermal resource in the field. One of the most prominent uses of geochemical sampling is the determination of subsurface temperatures using Geothermometry. In this study, commonly accepted concepts of cationic and Silica Geothermometry have been applied to understand conditions in the reservoir. Interpretation of the data has given us some input on the reservoir characteristics like reservoir temperature, mixing process and multiple fluid origins. In Dholera geothermal field for determination of variation in hydrochemical facies and understanding of the advancement of the hydrochemical forms, the current research additionally imagines the significance of graphical portrayals like Piper chart, Scholler and so forth individually. The seawater intrudes the composition of water shows the reservoir. The physio-chemical properties of the water also influence exploitation strategies.

Natural hydrogen seeps called fairy circles have been identified in Mali, Brazil, Russia, and United-States. Natural hydrogen is produced from a field in Mali for 7 years. The natural hydrogen system is still poorly understood and needs to be studied in new geological contexts as to streamline hypotheses on the system dynamics and elements into concepts. In Australia, numerous circular surface features, commonly called salt lakes or swaps, are visible from the sky but no existing work shows nor quantifies any hydrogen content in those features. In this study, we reviewed the existing literature on fairy circles as to determine and directly test with soil-gas measurements if hydrogen surface-emitting features are present in Australia. We determined best candidates to test with a multi-disciplinary approach linking geology, multi-physical imaging, and seismic interpretation. Soil-gas measurements showed persistent hydrogen concentration localized in the external ring of circular depressions aligned along the Darling Fault, a major crustal boundary between the granitic, mafic and ultramafic rocks of the Yilgarn Craton from the sedimentary rocks of the Perth Basin. This work is the proof that fairy circles, in the meaning of H2 emitting structures, are present in Australia and opens the door to new prospectivity pathways by evaluating original hypotheses on natural hydrogen generation, migration pathways and entrapment. This geological setting promotes deep serpentinization of ultramafic rocks as well as oxidation of iron-rich Archean rocks and mafic dikes as potential hydrogen sources that are both of massive potential economic value. This hydrogen can circulate and be entrapped as aqueous hydrogen in low-salinity aquifers or migrate in gaseous phase in fault zones up to intermediate structural reservoir or to the surface.

The traditional convolutional neural networks applied in mineral prospectivity mapping usually extract features from only one scale at each iteration, resulting in plain features. To combat this limitation, this study utilized a convolutional neural network based on GoogLeNet to predict prospectivity for gold deposits in the Fengxian study area, China. The GoogLeNet adopted four groups of convolution kernels to extract and integrate features from multiple scales, obtaining abundant and comprehensive features related to mineralization. According to a multi-source geoinformation analysis, we selected 11 exploration criteria, including three geological factors (NW-trending brittle-ductile faults, NE-trending brittle faults, and anticline axes) and eight geochemical exploration data layers (Au, Ag, As, Hg, Pb, Zn, Cu, and Sb). Then, we created predictor samples to train the models to mine evidential features. Following to a comprehensive analysis, we formed a fusion model of GoogLeNet for mineral prospectivity modeling. The results demonstrated that the fusion model achieved an optimized predictive accuracy of 93.1% and an area under curve of 0.968. This fusion model outperformed the other models with superior success rate and prediction area rate performances, capturing 72% of the known gold deposits in just 27.3% of the research area. The results indicate the effectiveness of GoogLeNet in mineral prospectivity mapping. Finally, we classified the Fengxian district into three areas according to their different mineral prospectivity. The high-prospectivity areas provide significant implications for further exploration of gold deposits in the study area.

Studies of near-seabed datasets show that salt tectonics controls the distribution and architecture of deep-water reservoirs in many salt-influenced basins. It is typically difficult, however, to study the distribution and stratigraphic evolution of depositional systems preserved at deeper, economically significant depths, reflecting poor seismic imaging of steeply dipping strata flanking high-relief salt structures. 3D seismic and borehole data from the Santos Basin, offshore Brazil allow us to identify a range of depositional elements that form the building blocks of three main tectono-stratigraphic phases. During the first phase, channel systems and lobes were confined within updip minibasins and to the hangingwalls of salt-detached faults. During the second phase, channel systems and lobes filled updip minibasins to bypass sediment downslope, with coarse clastic deposition then occurring in downdip minibasins, >100 km from the coeval shelf margin. Syndepositional seafloor relief caused: (i) channel system deflection and diversion around salt-cored highs; (ii) channel system uplift and rotation on the flanks of rising salt structures; (iii) lateral and frontal confinement of channel systems. During the final phase, rising salt walls dissected previously deposited deep-water systems, with MTCs deposition becoming increasingly important. Our results have important implications for post-salt prospectivity in the Santos Basin and other salt-influenced sedimentary basins, with a range of reservoirs and trapping styles present in this underexplored interval. More specifically, we show that large volumes of clastic sediment were not trapped behind the ‘Albian Gap’, a salt-controlled depocenter dominating the north-western basin margin, but were instead delivered further basinward.

Mineral prospectivity mapping (MPM) is a fundamental task in mineral exploration. The logistic regression (LR) method has been widely used as a data-driven tool for MPM because it is compact and straightforward, and it presents explicit explanations. However, there exists the inherent deficiency of linear assumption in the ordinary LR method. In order to overcome this disadvantage, a hybrid model (i.e., hybrid GEP–LR) that combines LR with gene expression programming (GEP) has been developed. It uses the evolutionary algorithm of GEP to discover automatically the symbolic function defined in the logit transformation of the ordinary LR method without any assumption. To investigate the performance of the hybrid GEP–LR hybrid method, a synthetic data modeling and a case study of prospectivity mapping for porphyry Cu–Mo polymetallic deposits (PCMPDs) in the Eastern Tianshan region, northwestern China are presented here. In the synthetic data modeling, two different datasets, namely linearly separable dataset and nonlinearly separable dataset, were generated by Monte Carlo simulation. By defining the area under the receiver operating characteristic curve (AUC) as a fitness function, these two datasets were used to validate the efficiency of the hybrid GEP–LR method in classification of both linearly and nonlinearly separable datasets. In the case study, 15 hybrid GEP–LR models were designed using 15 different evolutionary strategies of the GEP algorithm including the number of genes, the head length of every gene, and the set of functions. Binary maps of prospectivity for PCMPDs derived using these 15 hybrid GEP–LR models with the fitness function of AUC were compared and contrasted with the one derived using the ordinary LR method. The results demonstrate that the proposed hybrid GEP–LR method is a robust data-driven tool that can deal with both linear and nonlinear problems in data mining. It outperforms the ordinary LR method in predicting target areas for mineral prospecting. GEP provides an inspiring way to combine other machine learning methods in addressing challenges associated with supervised or unsupervised classification issues with respect to complex nonlinearly distributed geodata.

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 porphyry Cu ± Mo-Au and Late Cretaceous porphyry Mo deposits, both with surrounding base metal enrichments in associated skarn, greisen, and vein occurrences. To help assess Mo resource potential in the arc-related magmatic–hydrothermal systems, we evaluated geochemical data from 1831 stream sediment samples that were collected at a density of one sample per 2 km2 during the Chinese National Geochemical Mapping Program. Singularity analyses were combined with factor analysis and singularity-quantile methods to interpret distribution patterns for 18 ore-related elements and to define geochemical signatures in the stream sediment data related to areas of high favorability for the presence of Cu- and Mo-dominant ores in the Zhongdian arc. The known mineral occurrences in the arc show spatial-frequency singularity. Spatial patterns in the sediment data indicate the Triassic Cu-dominant systems are associated with intrusions emplaced along a northwest-trending system of faults, whereas the Cretaceous Mo-rich ore systems are related to north–south striking structures. The patterns indicate locations of high favorability for new discoveries of Mo occurrences in the central part of the arc where many deposits are already known, as well as in the southwestern part of the arc that presently lacks abundant known occurrences. Factor analysis results show that the areas favorable for the Mo porphyries are best defined by a Mo–W–Bi–Pb–Ag signature in the sediment data. These can be distinguished from areas most likely to contain older Cu-rich systems that are marked by sediments with Cu, Zn, and Au anomalies. The singularity analysis, in combination with factor analysis and singularity-quantile, was able to successfully separate anomalies in the sediment samples that were representative of the Late Triassic and Late Cretaceous mineral systems, despite elements such as Mo, Pb, and Ag being enriched in ores of both ages.

Sedimentary basins in south Egypt, in particular Komombo and Nuqra basins, have been paid more attention during the last few decades, due to their hydrocarbon prospectivity. The present article is an integrated seismic and petrophysical study aiming at evaluating the hydrocarbon potentiality of the Cretaceous sedimentary sequence in these two basins. Data of four drilled wells including a conventional core, and well log data, in addition to 20 2D seismic profiles (ten for each basin), were studied in detail. The study indicates that the sedimentary sequence in the two basins is represented by the Nubia Sandstone, which is subdivided (from base to top) into Komombo, Six Hills, Abu Ballas, Sabaya, and Maghrabi formations. This sequence in both basins is dissected by a set of NW–SE-trending faults, in addition to a younger set of NNE–SSW to NE–SW trending. The present study indicates that the Komombo Formation is characterized by a good oil potentiality of a net pay thickness up to 65 ft (1 ft = 0.3048 m) in the Komombo basin. The Six Hills Formation may also be prospective, especially near the depocenters of the Komombo and Nuqra basins, due to its wide extension, good thickness (up to 4150 ft), and presence of some oil shows. However, the reservoir of the studied sequence may be of low quality due to cementation with silica, compaction, and the presence of high shale content. Two reservoir rock types (RRTs) were assigned to the Six Hills Formation: RRT1 (clean siliceous sandstone) with good reservoir quality and RRT2 (argillaceous sandstone) with low reservoir quality.

Successful delineation of high potential targets for exploration in maturely-explored orefields is still a tough challenge. A reliable prediction model achieved by integration of various ore-related geological factors and exploration information in the 3D space is an effective approach to deal with this challenge. The Anqing orefield has been intensively exploited for decades, and thereby the possible potential left must be at depth. The accumulated abundant data of exploration and research provide us a possibility for carrying out machine learning-based 3D modeling. The 3D block models of the main geological bodies, resistivity and volumetric strain field in this orefield were used as multi-resource geological data to construct prediction models by using weight-of-evidence and machine learning methods. Through performance evaluation and comparison, the following conclusions were obtained: (1) it is more scientific and reasonable to use all the geological prospecting factors concurrently for mineral prospectivity mapping (MPM) rather than use one or a part of them; (2) random forest (RF) algorithm seems capable of MPM because of its high accuracy and reliability in prediction; and (3) rational selection of training and learning samples, especially, those from actual geological objects and exploration engineering, plays a more critical role in MPM than algorithm and methods themselves. Two different RF prediction models were obtained for MPM in the east and the surrounding part of this orefield based on the outcome of geophysical prospecting. The spaces with prediction probabilities higher than 0.508 in the east part and 0.501 in the surroundings take up only 3.71% volume of the whole orefield, but contain 95.92% of the mineralized blocks. The high potential targets are most likely parts of the above spaces with high prediction probabilities that have not been drilled yet up to now.

Rare earth element and yttrium (REY) deposits are important strategic resources widely used in high-tech applications, such as solar cells and wind turbines. This paper summarises the temporal-spat...

Known mineralized locations and randomly chosen non-mineralized locations are used traditionally as training samples in data-driven mineral prospectivity mapping (MPM). In this paper, we took advantage of (a) the variable importance and partial dependence plot, which enable interpretation of random forest (RF) modeling, and (b) the synthetic minority over-sampling technique, and investigated the efficacy of outlier-based training samples used for data-driven MPM in contrast to traditional practice of using known mineralized locations as positive training samples. The prediction maps obtained by RF modeling based on different sets of training samples suggest bias toward known mineralized locations in data-driven MPM. The proposed outlier-based training samples for data-driven MPM involve both unsupervised learning and supervised learning. The former aims at outlier detection, while the latter uses the resulting outliers as positive training samples to investigate the following: firstly, the delineation of prospective area or spatial signature of existing mineral system by avoiding the bias arising from the known mineralized locations in data-driven MPM and secondly, the coherence of spatial signature of outliers, which justifies their feasibility as positive training samples for data-driven MPM by RF modeling. Analyses of receiver operating curves and correlations of the resulting prediction maps imply that outliers derived by isolation forest show consistent spatial signature as the known mineralized location and, thus, were effective in narrowing down the prospective target areas just like in traditional data-driven MPM.

Mafic-ultramafic subvolcanic intrusive complexes, at the 1–10-km scale, constitute favorable environments for the formation of orthomagmatic Ni-Cu-PGE sulfide deposits. Within 100–1000-km scale large igneous provinces (LIPs), the most favorable economic target areas may be difficult to identify using a traditional regional exploration approach. We report results of field and laboratory studies focused on an integrated geochemical-architectural model for the Ni-Cu-PGE sulfide potential of the High Arctic large igneous province (HALIP) of Canada, the largest and best-exposed portion of this circum-arctic LIP. Previous lithogeochemical studies of the Canadian HALIP (130–80 Ma) concluded that subalkaline mafic units located on Axel Heiberg Island were more prospective than younger, alkalic igneous rocks (alkaline basalts to basanites) exposed on northern Ellesmere Island. Mapping carried out for this study revealed the presence of abundant subalkaline igneous rocks on western Ellesmere Island. We interpret these units as an extension of the Schei Sills, a stratigraphically bound sequence of intrusions exposed along a ~ N-S belt on eastern Axel Heiberg Island. Samples from the Schei Sills and from the Middle Fiord Intrusive Complex (~ 20 km2, central Axel Heiberg Island) show Cu/Zr values above and below unity, consistent with complementary sulfide enrichment and removal, respectively, within these intrusive systems. The spatial association of the Schei Sills with the post-emplacement Eocene Stolz Thrust is noteworthy, as later thrusting associated with the Paleogene Eurekan Orogeny caused the uplift of deeper parts of the HALIP subvolcanic feeder system and associated wall-rock stratigraphy. Absolute Ir-group PGE contents are low throughout the HALIP; however, Pt-group PGE contents range widely among analyzed tholeiitic intrusive rocks. For example, relatively elevated Pt + Pd values of 10–30 ppb in samples from the Middle Fiord Intrusive Complex and the Schei Sills suggest heightened prospectivity in these two areas. In contrast, tholeiitic flood basalts in the Strand Fiord Formation and associated intrusions exposed in western Axel Heiberg Island (~ 95 Ma) exhibit Cu/Zr

This study illustrates the application of a geometric average integration of aeromagnetic, radiometric and satellite imagery data over a region prone to Cu‐bearing mineralization at Chahargonbad area in Kerman province of Iran. Processing aeromagnetic, radiometric and Advanced Spaceborne Thermal Emission and Reflection Radiometer satellite data can provide exploratory insights about favourable zones in association with porphyry‐type ore occurrences, which can be synthesized through a combination of knowledge‐ and data‐driven approaches as a geometric average and be represented in a mineral prospectivity map. The existence of known deposits in a prospect region can facilitate the investigation of significant exploratory footprints extracted from airborne data by calculating each indicator layer's weight by plotting a prediction–area curve accompanied by a concentration–area fractal curve. Among various indicators, the most important ones are determined based on derived weights from the prediction–area plots to be synthesized in a single Cu favorability map. To fulfil this aim, indicator layers from airborne geophysics (magmatic bodies, magnetic lineaments and potassium radiometry) and remote‐sensing data (alterations such as argillic, phyllic, propylitic and iron oxide along with geological lineaments) were prepared and evaluated using the known porphyry Cu mineralization by the simultaneous plot of the concentration–area fractal model and the prediction–area curve to attain the ore prediction rate and the relevant occupied area of each map for weight assignment of indicators. The geometric average prospectivity model was applied to synthesize the leading indicators, and the result was compared with a multi‐class index overlay map. This study's significance lies in improvement of the performance of the mineral prospectivity/potential mapping after running a geometric average by a higher ore prediction rate of 79%, which has occupied 21% of the area as potential zones for further mining investigations.

GIS-based mineral prospectivity mapping (MPM) is a computer-aided methodology for delineating and better constraining target areas deemed prospective for mineral deposits of a particular type. The underlying algorithms are well-established and well-understood, but on the whole, MPM that is a multi-faceted and multi-criteria approach, is faced with a high degree of uncertainty. We distinguish three principal types of uncertainties: (1) data-related (e.g., the sometimes erroneous, inadequate, incomplete, unevenly distributed or poorly resolved nature of the input data); (2) model-related (e.g., the diversity and inherent natural variability of mineral deposits, our lack of complete knowledge of the targeted mineral deposit type, and our imperfect ability to interpret geoscience datasets); and (3) judgment-related (e.g., the influence of cognitive heuristics and biases). In this contribution, we review and characterize the key uncertainties listed above and provide possible solutions as to how they may be recognized and mitigated in the context of MPM. This review also clearly illustrates the need for future studies designed to carefully monitor each step of the MPM process and aims at reducing uncertainty by, for example, (1) using carefully vetted, high-quality input data, (2) developing targeting models based on the best possible understanding of the underlying mineral deposit models and backed by machine learning-based simulations of likely ore-forming processes, and (3) adopting advanced methods such as deep learning algorithms for effective integrating of predictor maps.

In this paper, we explore unsupervised cluster analysis to aid mineral prospectivity mapping (MPM) in two aspects: (1) to cluster geochemical data for MPM based on detailed analysis of evidence maps and (2) to explore coherence of spatial signatures at/around mineralized locations as well as outliers of geochemical data. To do so, a systematic procedure is proposed based on the Iterative Self-organizing Data Analysis Techniques Algorithm (ISODATA). Through this procedure, the detailed analysis of evidence maps in Hezuo–Meiwu district, Gansu Province, China, which portray five selected geochemical elements, showed that clusters with and without mineralized locations provide insight to weighing of each evidence. Finally, through the integration of evidence maps, the favorability score map yielded high AUC (> 0.80) for delineating various mineralized locations in the study area, which proves the efficacy of unsupervised cluster analysis as an aid to MPM. Moreover, the coherence of spatial signatures of known mineralized locations, which comprise a training dataset, is vital to data-driven MPM. Groupings of mineralized locations based on the ISODATA and visual inspection supported by PCs from principal component analysis imply that different deposit types may share the same or similar spatial signature and outliers in geochemical data may be potential training samples used for data-driven MPM. Mineralized locations of the same deposit type may show significant dissimilarity. However, this provides insights into selecting mineralized/non-mineralized locations for creation of training datasets. Interestingly, in the study area, major mineralized locations in zones divided by regional fault are clustered separately into two groups. This result not only proves that cluster analysis is effective for exploring the coherence of spatial signatures at/around mineralized locations, but it also justified our previous study, whereby we performed MPM by zones using machine learning algorithms.

The Yilgarn Craton, Western Australia is endowed with significant gold, nickel‑copper, Cu-Pb-Zn volcanic-hosted massive sulphide (VHMS) and iron ore deposits. Detailed knowledge of the spatial relationships between mineral systems and the crustal framework of the Yilgarn Craton may be powerful tools in the predictive exploration of undiscovered mineral systems. Both Nd and Hf isotopes have the potential to image crustal residence, mantle tapping structures, and juvenile input over both space and time. This study uses a recent compilation of Hf (1236 zircon analyses) and Nd (462 whole rock analyses) isotopes to image the architecture of the crust and compare this to the distribution of gold, nickel‑copper-platinum group elements (Ni-Cu-PGEs), Cu-Pb-Zn VHMS and iron ore deposits. Spatial correlation plots reveal a statistically significant relationship between Nd and Hf isotopic domains and the occurrence of gold, Ni-Cu-PGE, base metals, and iron confirmed by the coefficient of determination (R2) which measures how differences in metal occurrences can be explained by differences in the isotopic signature. Nickel occurrences show the strongest antithetic spatial correlation (R2 = 0.80) with Hf model ages, followed by gold (R2 = 0.58) and base metals (R2 = 0.35), which implies higher metal endowment in areas of more juvenile crust. In contrast, there is a significant positive spatial correlation (R2 = 0.49) between iron ore occurrence and older model ages. Isotopic data can also be plotted as Hf or Nd time-slices, where the strongest spatial correlation between gold, nickel, and base metal occurrence and juvenile input occurs at ca. 2700–2550 Ma. Generally, a model can be proposed where (1) base metals are more prevalent in juvenile crust because of its thinner nature, greater mantle input, high-heat flow, increased water-magma interaction; (2) iron ore is concentrated in regions of old, mature, and stable crust; (3) komatiite-hosted Ni-Cu-PGE systems are focused in juvenile rift zones at the edge of old crustal blocks; and (4) gold is also localised in juvenile crust at craton edges where several factors are localised, including orogenesis, occurrence of previously fertilised crust (i.e. Fe-rich lithologies, sanukitoids), favourable structures (potential inversion of rift structures), and ‘late’ alkaline magmatism. In gold, Ni-Cu-PGE, and base metal mineral systems, the ability of magmas to access higher crustal levels unimpeded appears vital in regional prospectivity and is itself a function of crustal configuration.

This paper reports an application of uncertainty visualisation of a regional scale (1:50 000) 3D geological geometry model to be involved in GIS-based 3D mineral potential assessment of the Xiangxibei lead-zinc mineral concentration area in northwestern Hunan District, China. Three-dimensional (3D) geological modelling is a process of interpretation that combines a set of input measurements in geometry. Today, technology has become a necessary part of GIS-based deep prospecting. However, issues of sparse data and imperfect understanding exist in the process so that there are several uncertainties in 3D geological modelling. And these uncertainties are inevitably transmitted into the post-processing applications, such as model-based mineral resource assessment. Thus, in this paper, first, a big-data-based method was used to estimate the uncertainty of a 3D geological model; second, a group of expectations of geological geometry uncertainty were calculated and integrated into ore-bearing stratoisohypse modelling, which is one of the major favourable parameters of assessment for Lead-Zinc (Pb-Zn) deep prospectivity mapping in northwestern Hunan; and finally, prospecting targets were improved.

Today’s era of big data is witnessing a gradual increase in the amount of data, more correlations between data, as well as growth in their spatial dimension. Conventional linear statistical models applied to mineral prospectivity mapping (MPM) perform poorly because of the random and nonlinear nature of metallogenic processes. To overcome this performance degradation, deep learning models have been introduced in 3D MPM. In this study, taking the Huayuan sedimentary Mn deposit in Hunan Province as an example, we construct a 3D digital model of this deposit based on the prospectivity model of the study area. In this approach, 3D predictor layers are converted from the conceptual model and employed in a 3D convolutional neural network (3D CNN). The characteristics of the spatial distribution are extracted by the 3D CNN. Subsequently, we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3D CNN model and weight of evidence (WofE) method on each group. The predictive model is trained on the basis of the coupling correlation between the spatial distributions of the variables and the underground occurrence space of the Mn orebodies, and the correlation between different ore-controlling factors. The analysis of 12 factors indicates that the 3D CNN model performs well in the 3D MPM, achieving a promising accuracy of up to 100% and a loss value below 0.001. A comparison shows that the 3D CNN model outperforms the WofE model in terms of predictive evaluation indexes, namely the success rate and ore-controlling rate. In particular, the 1–12 ore-controlling factors selected in experiment 5 provide a significantly better prediction effect than the other factors. Consequently, we conclude that the Mn deposit in the study area is not only related to the stratum and interlaminar anomalous bodies but also to the spatial distribution of the faults. The experimental results confirm that the proposed 3D CNN is promising for 3D MPM as it eliminates the interference factors.

With the decrease in surface and shallow ore deposits, mineral exploration has focused on deeply buried ore bodies, and large-scale metallogenic prediction presents new opportunities and challenges. This paper adopts the predictive thinking method in this era of big data combined with specific research on the special exploration and exploitation of deep-earth resources. Four basic theoretical models of large-scale deep mineralization prediction and evaluation are explored: mineral prediction geological model theory, multidisciplinary information correlation theory, mineral regional trend analysis theory, and mineral prediction geological differentiation theory. The main workflow of large-scale deep resource prediction in the digital and information age is summarized, including construction of ore prospecting models of metallogenic systems, multiscale 3D geological modeling, and 3D quantitative prediction of deep resources. Taking the Lala copper mine in Sichuan Province as an example, this paper carries out deep 3D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.

This paper demonstrates knowledge-guided fuzzy logic modeling of regional-scale surficial uranium (U) prospectivity in British Columbia (Canada). The deposits/occurrences of surficial U in this region vary from those in Western Australia and Namibia; thus, requiring innovative and carefully-thought techniques of spatial evidence generation and integration. As novelty, this papers introduces a new weighted fuzzy algebraic sum operator to combine certain spatial evidence layers. The analysis trialed several layers of spatial evidence based on conceptual mineral system model of surficial U in British Columbia (Canada) as well as tested various models of evidence integration. Non-linear weighted functions of (a) spatial closeness to U-enriched felsic igneous rocks was employed as U-source spatial evidence, (b) spatial closeness to paleochannels as fluid pathways spatial evidence, and (c) surface water U content as chemical trap spatial evidence. The best models of prospectivity created by integrating the layers of spatial evidence for U-source, pathways and traps predicted at least 85% of the known surficial U deposits/occurrences in >10% of the study region with the highest prospectivity fuzzy scores. The results of analyses demonstrate that, employing the known deposits/occurrences of surficial U for scrutinizing the spatial evidence layers and the final models of prospectivity can pinpoint the most suitable critical processes and models of data integration to reduce bias in the analysis of mineral prospectivity.

The renewed quest to boost Nigeria’s dwindling reserves through aggressive search for oil and gas deposits in Cretaceous sedimentary basins has re-ignited the need to re-evaluate the hydrocarbon potentials of the Dahomey Basin. Aeromagnetic data are a low-cost geophysical tool deployed in mapping regional basement structures and determination of basement depths and sedimentary thickness in frontier basin exploration. In this study, high-resolution aeromagnetic (HRAM) data covering the Dahomey Basin Nigeria have been interpreted to map the basement structural configuration and to identify mini-basins favorable for hydrocarbon prospectivity. The total magnetic intensity grid was reduced to the equator (RTE) and edge detection filters including first vertical derivative (FVD), total horizontal derivative (THDR), tilt derivative (TDR) and total horizontal derivative of upward continuation (THDR_UC)) were applied to the RTE grid to locate the edges and contacts of geological structures in the basin. Depth to magnetic sources were estimated using the source parameter imaging (SPI) method. Data interpretation results revealed shallow and deep-seated linear features trending in the NNE-SSW, NE-SW, NW-SE and WNW-ESE directions. The SPI map showed a rugged basement topography which depicted a horst-graben architecture on 2D forward models along some selected profiles. Two mini-basins ranging in basement depths between 4.5 – 6.3km were mapped offshore of the study area. It appears the offshore Dahomey Basin holds greater promises for hydrocarbon occurrence due to the presence of thicker succession of sedimentary deposits in the identified mini-basins.

Mapping mineral prospectivity (MPM) is mostly beset with prediction uncertainties, which are generally categorized into (a) stochastic and (b) systemic types. The stochastic type is usually linked to the low quality as well as insufficiency/inefficiency of data used. In contrast, inaccurate selection of exploration criteria, exaggerated and arbitrary weighting of spatial evidence layers resulting from subjective judgment of analyst and applying an integration methodology, which is not able to consider the complexities of geological processes, are main sources of systemic type. This paper aims for reducing the second type of MPM uncertainty in delineating favorable exploration targets for Cu-Au mineralization in the Moalleman District, NE Iran. Thus, several efficient evidence layers were translated from geospatial criteria (e.g., geochemical, geological, structural and hydrothermal alterations) and were considered for integration purpose in the study area. Then, an improved data-driven simple additive weight (data-driven SAW) procedure was introduced for generating prospectivity model. In this procedure, prediction-area plots and frequency ratio method were applied for assigning objective weights to efficient evidence layers and their corresponding classes, respectively. Furthermore, a supervised algorithm for machine learning classification namely support vector machine (SVM) with radial basis function kernel was executed for comparison purposes. The results indicated that the two prospectivity models are succeeded in delineating favorable targets of mineralization; however, the SVM model is more reliable than data-driven SAW in predicting high-potential areas of mineralization.

Rock properties in the subsurface are of major importance for evaluating the petroleum prospectivity of a sedimentary basin. The key rock properties to understand are porosity, density, temperature, effective stress, and pore pressure. These rock properties can be obtained or calculated when borehole data are available. However, borehole data are usually sparse, especially in frontier basins. We propose some simple rock-physics transforms for converting P-wave velocity to other rock properties. We found that these rock-physics transforms are predictive in the east and west sides of Campeche Basin. The proposed rock-physics transforms can be used to obtain laterally varying rock properties based on information derived from seismic data.

In recent years, various geological activities and different mineral prospecting and exploration programs have been intensified along the Red Sea hills in order to elucidate the geological maps and to evaluate the mineral potentials. This study is therefore aimed at testing the viability of using remote sensing and geographic information system (GIS) techniques for geological mapping and prospecting for gold mineralization in the area. The study area is located in northeast Sudan and covers an area of about 1379 km2. Different digital image processing techniques were applied to Landsat 8 Operational Land Imager image in order to increase the discrimination between various lithological units and to delineate wall rock alteration which represents target zones for gold mineralization. Image sharpening was performed to enhance the spatial resolution of the images for more detailed information. Contrast stretching was applied after the various digital processing procedures to produce more interpretable images. The principal component analysis transformations yielded saturated images and resulted in more interpretable images than the original data. Several ratio images were prepared, combined together and displayed in RGB color composite ratio images. This process revealed the existence of alteration zones in the study area. These zones extend from the northeast to the southwest in the acid meta-volcanic and silica barite rocks. The enhanced satellite images were implemented in the GIS environment to facilitate the final production of the geological map at scale 1:400,000. X-ray fluorescence analyses prove that selected samples taken from the wall rock alteration zones are gold-bearing.