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Definition of the basement architecture within the Ceduna Sub-basin is poorly understood due to the depth of the basin and limited deep crustal geophysical datasets. This study uses seismic interpr...

Timor’s Lolotoi Metamorphic Complex outcrops regionally on Timor and is critical to the interpretation of hydrocarbon prospectivity, which is obscured by complex geology. Reprocessing of 1994 data ...

In data-driven mineral prospectivity mapping, a statistical model is established to represent the spatial relationship between layers of metallogenic evidence and locations of known mineral deposits, and then, the former are integrated into a mineral prospectivity model using the established model. Establishment of a data-driven mineral prospectivity model can be regarded as a process of searching for the optimal integration of layers of metallogenic evidence in order to maximize the spatial relationship between mineral prospectivity and the locations of known mineral deposits. Mineral prospectivity can be simply defined as the weighted sum of layers of metallogenic evidence. Then, the optimal integration of the layers of evidence can be determined by optimizing weight coefficients of the layers of evidence to maximize the area under the curve (AUC) of the defined model. To this end, a bat algorithm-based model is proposed for data-driven mineral prospectivity mapping. In this model, the AUC of the model is used as the objective function of the bat algorithm, and the ranges of the weight coefficients of layers of evidence are used to define the search space of the bat population, and the optimal weight coefficients are then automatically determined through the iterative search process of the bat algorithm. The bat algorithm-based model was used to map mineral prospectivity in the Helong district, Jilin Province, China. Because of the high performance of the traditional logistic regression model for data-driven mineral prospectivity mapping, it was used as a benchmark model for comparison with the bat algorithm-based model. The result shows that the receiver operating characteristic (ROC) curve of the bat algorithm-based model is coincident with that of the logistic regression model in the ROC space. The AUC of the bat algorithm-based model (0.88) is slightly larger than that of the logistic regression model (0.87). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets optimally delineated by using the bat algorithm-based model and logistic regression model account for 8.10% and 11.24% of the study area, respectively, both of which contain 79% of the known mineral deposits. These results indicate that the performance of the bat algorithm-based model is comparable with that of the logistic regression model in data-driven mineral prospectivity mapping. Therefore, the bat algorithm-based model is a potentially useful high-performance data-driven mineral prospectivity mapping model.

The structural context and Australian versus Asian affinity of regional outcrop of Timor’s Lolotoi Metamorphic Complex are critical to the interpretation of hydrocarbon prospectivity but are obscur...

In the past few decades, a variety of data-driven predictive modeling techniques has led to a dramatic advancement in mineral prospectivity mapping (MPM). The random forests (RF) algorithm, a machine learning method, has been applied successfully to data-driven MPM. However, there are two main challenges that need to be examined. Firstly, whether RF modeling can be used for the 3D MPM. The voxel (in 3D) has replaced the pixel (in 2D) to represent geological features, and so the capability of the RF model should be tested. Secondly, when we conduct regional-scale MPM, building a suitable conceptual model has a significant influence on the results; however, mineral deposit models often focus on just deposit-scale features. These two challenges were encountered in the case study in the Tongling ore cluster, which is the most representative skarn ore-concentrated area in the Middle–Lower Yangtze River Valley Metallogenic Belt in Eastern China. Thus, 3D geological models of the Tongling ore cluster were constructed from the multiple geological datasets. Then, a conceptual model was translated into 3D predictor layers. Finally, we tested and compared the MPM capabilities of the RF and compared it with weights-of-evidence (WofE) modeling. The results indicate that RF modeling not only outperforms WofE modeling in 3D MPM, but it also has capability to assess the relative importance of different predictor layers. Further testing of this method is warranted in other areas with different scales or metallogenic model to investigate fully its efficiency.

This paper describes the application of an unsupervised clustering method, fuzzy c-means (FCM), to generate mineral prospectivity models for Cu +/- Au +/- Fe mineralization in the Feizabad District of NE Iran. Various evidence layers relevant to indicators or potential controls on mineralization, including geochemical data, geological-structural maps and remote sensing data, were used. The FCM clustering approach was employed to reduce the dimensions of nine key attribute vectors derived from different exploration criteria. Multifractal inverse distance weighting interpolation coupled with factor analysis was used to generate enhanced multi-element geochemical signatures of areas with Cu +/- Au +/- Fe mineralization. The GIS-based fuzzy membership function MSLarge was used to transform values of the different evidence layers, including geological-structural controls as well as alteration, into a [0-1] range. Four FCM-based validation indices, including Bezdek's partition coefficient (V-Pc) and partition entropy (V-Pe) indices, the Fukuyama and Sugeno (V-FS) index and the Xie and Beni (V-XB) index, were employed to derive the optimum number of clusters and subsequently generate prospectivity maps. Normalized density indices were applied for quantitative evaluation of the classes of the FCM prospectivity maps. The quantitative evaluation of the results demonstrates that the higher favorability classes derived from V-FS and V-XB (N-d=9.19) appear more reliable than those derived from V-Pc and V-Pe (N-d=6.12) in detecting existing mineral deposits and defining new zones of potential Cu +/- Au +/- Fe mineralization in the study area.

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.

Modern mineral exploration involves making discoveries in geological environments where detecting deposits is increasingly difficult. This study presents an integrated, knowledge-driven prospectivi...

The Albertine Graben, an active sedimentary petroliferous basin, has gained global attention as the unexplored areas are recently being targeted for hydrocarbon prospectivity. Here, we present the first detailed geological investigation of the Upper Miocene–Pliocene clastic interval in the southern Lake Albert, part of the Semliki Basin. We employed an integrated approach that includes source rock evaluation, reservoir characterization, pore pressure, and geomechanical evaluation. Thermal maturity analyzed from vitrinite reflectance, spore color index, and Rock–Eval Tmax indicates that the Lower Kasande–Kakara shales are into the early catagenetic maturity, and the onset of oil window occurs at around 2550 m. With 1.8–2.4% total organic carbon content and dead carbon-free hydrogen index of ~ 600 mg S2/g TOC, a Type I/II oil-prone source rock was inferred, while the state of the thermal maturity reflects on the relatively low free oil yields associated with a poor oil production index. The quartz arenite reservoirs of the Upper Miocene Kasande–Kakara Formation exhibit excellent petrophysical characteristics and possess pore pressure gradients of 0.17–0.24 psi/ft (1 psi/ft = 22.6206 Mpa/km) in two distinct zones (2040.7–2221.5 m and 2554.7–2730 m) with gross vertical thickness of ~ 206 m. The region belongs to a normal faulting tectonic regime where the vertical stress gradient is 0.91–0.93 psi/ft with a lower bound of minimum horizontal stress gradient interpreted as 0.62 psi/ft. The hydrostatically pressured Miocene shales have higher shear failure gradients and exhibited extensive wellbore failures. The implications of geological characterization in both hydrocarbon exploration and future drilling in the basin are envisaged in this research.

Machine learning (ML) as a powerful data-driven method is widely used for mineral prospectivity mapping. This study employs a hybrid of the genetic algorithm (GA) and support vector machine (SVM) model to map prospective areas for Au deposits in Karamay, northwest China. In the proposed method, GA is used as an adaptive optimization search method to optimize the SVM parameters that result in the best fitness. After obtaining evidence layers from geological and geochemical data, GA–SVM models trained using different training datasets were applied to discriminate between prospective and non-prospective areas for Au deposits, and to produce prospectivity maps for mineral exploration. The F1 score and spatial efficiency of classification were calculated to objectively evaluate the performance of each prospectivity model. The best model predicted 95.83% of the known Au deposits within prospective areas, occupying 35.68% of the study area. The results demonstrate the effectiveness of the GA–SVM model as a tool for mapping mineral prospectivity.

Over the last two decades, Mineral Resources Tasmania has been developing regional 3D geological and geophysical models for prospective terranes at a range of scales and extents as part of its suite of precompetitive geoscience products. These have evolved in conjunction with developments in 3D modeling technology over that time. Commencing with a jurisdiction-wide 3D model in 2002, subsequent modeling projects have explored a range of approaches to the development of 3D models as a vehicle for the better synthesis and understanding of controls on ore-forming processes and prospectivity. These models are built on high-quality potential field data sets. Assignment of bulk properties derived from previous well-constrained geophysical modeling and an extensive rock property database has enabled the identification of anomalous features that have been targeted for follow-up mineral exploration. An aspect of this effort has been the generation of uncertainty estimates for model features. Our experience is that this process can be hindered by models that are too large or too detailed to be interrogated easily, especially when modeling techniques do not readily permit significant geometric changes. The most effective 3D modeling workflow for insights into mineral exploration is that which facilitates the rapid hypothesis testing of a wide range of scenarios whilst satisfying the constraints of observed data.

A two-stage fuzzy inference system (FIS) is applied to prospectivity modelling and exploration-target delineation for REE deposits associated with carbonatite-alkaline complexes in western part of the state of Rajasthan in India. The design of the FIS and selection of the input predictor map are guided by a generalised conceptual model of carbonatite-alkaline-complexes-related REE mineral systems. In the first stage, three FISs are constructed to map the fertility and favourable geodynamic settings, favourable lithospheric architecture, and favourable shallow crustal (near-surface) architecture, respectively, for REE deposits in the study area. In the second stage, the outputs of the above FISs are integrated to map the prospectivity of REE deposits in the study area. Stochastic and systemic uncertainties in the output prospectivity maps are estimated to facilitate decision making regarding the selection of exploration targets. The study led to identification of prospective targets in the Kamthai-Sarnu-Dandeli and Mundwara regions, where project-scale detailed ground exploration is recommended. Low-confidence targets were identified in the south of the Siwana ring complex, north and northeast of Sarnu-Dandeli, south of Barmer, and south of Mundwara. Detailed geochemical sampling and high-resolution magnetic and radiometric surveys are recommended in these areas to increase the level of confidence in the prospectivity of these targets before undertaking project-scale ground exploration. The prospectivity-analysis workflow presented in this paper can be applied to delineation of exploration targets in geodynamically similar regions globally such as Afar province (East Africa), Paraná-Etendeka (South America and Africa), Siberian (Russia), East European Craton-Kola (Eastern Europe), Central Iapetus (North America, Greenland and the Baltic region), and the Pan-superior province (North America).

Thanks to the brilliant progress in machine learning, many research works have conducted data-driven mineral prospectivity mapping. However, it is challenging to integrate highly multidisciplinary geoscientific data with machine learning algorithms. Especially, geological data are heterogeneous and non-numerical even though they are crucial for mineral exploration. In this work, we introduce how to preprocess the geoscientific data and design a machine learning model based on knowledge to make the best use of both geoscientific information and the advantages of machine learning. We focus on the region-scale prospectivity mapping for the komatiite-hosted nickel in Yilgarn craton, Western Australia. We extract second and thirdorder features from geophysical data to enable machine learning models to capture various patterns of mineral deposits. In terms of geology, faults, interpreted geology, and isotopic mapping data are converted into numerical features that could be related to the komatiite-hosted nickel deposits. Based on domain knowledge, we design a deep learning model that systemically combines geophysical and geological features. First, our model generates a feature map and initial prospectivity map using geological data and geophysical worms which could reveal the crustal structures. Next, the model produces a final prospectivity map that delineates potential komatiite-hosted nickel deposits using whole data including geophysics. The model is trained with the locations of the known nickel deposits. We divide the Yilgarn craton area into a train and test region to validate our model. We adopt the AUC score and prospectivity score percentile of known deposits to evaluate our model in various aspects. Our model achieved a high AUC and percentile score and it can be efficiently used for early-stage nickel exploration. The suggested workflow could be applied to the exploration of the other mineral types with a slight modification reflecting the characteristics of the mineralizations., Open-Access Online Publication: March 01, 2023

he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

The accuracy of data-driven predictive mineral prospectivity models relies heavily on the training datasets used. These models are usually trained using data for “known” deposit locations as well as “non-deposit” locations that are based on randomly generated point patterns. In this study, data related to the Seabridge Gold Inc Iskut project, an epithermal Au deposit in northwestern British Columbia (BC), Canada, are used to test the utility of data-driven mineral prospectivity modeling. The input spatial dataset is comprised mostly of publicly available data. Data for 18 vein and epithermal Au known mineral occurrences (KMO) are obtained from the BC Geological Survey’s MINFILE repository and selected as training deposit locations. A total of eleven sets of non-deposit locations (NDL) were also created, including one set of selected non-prospective KMO for Au deposits from the MINFILE and ten sets of random point patterns. Given the scale of this study, most of the KMO recorded on the property are of the epithermal deposit type. Hence, they could not be used as a selection criterion. Data-driven mineral potential models are generated using the random forest (RF) algorithm and trained on multiple data sets. The comparison of RF models demonstrated that using non-prospective KMO generates more accurate predictions than the random point pattern. The produced mineral prospectivity maps delineated multiple areas with higher discovery potential, which matched viable targets for the Au-Cu epithermal-porphyry system identified through previous Seabridge Gold Inc. (Toronto, ON, Canada) field reconnaissance and drilling programs.

In this paper, sequential Gaussian simulation (SGS) and number–size (N–S) fractal modeling were used for copper geochemical anomaly mapping in the western part (training area) of Kuhsiah-e-Urmak area, Iran. Then, according to the generated anomaly model in the training area, mineral potential mapping (MPM) was performed for the entire study area based on a well-fitted regression model as a data-driven method. In order to select the best model, six multivariate regression models including two linear, two quadratic, and two cubic functions were examined. For developing a mineral potential map of the study area, the geochemical anomaly raster map of the training area was utilized to create six models based on the values of geo-data sets. According to the results of $$R^{2}$$,$$R_{\text{adj}}^{2}$$, and $${\text{EF}}$$, the fourth model, generated using a quadratic function, was found to be the superior model compared with the rest of regression models defined in this paper. Based on the mathematical formula derived for the superior model, the geo-exploration data sets were synthesized to generate a potential map showing favorable areas for prospecting copper. The potential map generated was verified by the results of lithogeochemical sampling conducted in the training area and field observations in the entire study area.

The present work combines Concentration-Area (C-A) fractal model with the Fuzzy Analytical Hierarchy Process (FAHP) on a GIS platform for mapping gold potential in the Sonakhan Greenstone Belt, India. A set of stream sediment geochemical data obtained over a part of the study area was used to delineate potential regions of gold mineralisation. In order to develop a suitable predictive model, the gold exploration target was taken as target (dependent variable) and concentration of pathfinder elements of gold such as Au, As, Ag, Hg, Sb and Se in the stream sediments were used as predictors (independent variables). The C-A fractal model was applied to the geochemical data of each element to decompose anomaly and background components of the spatial dispersion of element concentration in the study area. AHP combined with fuzzy set theory (FAHP) was used to determine the priority or, the weight of each evidential geochemical anomaly map. The Gold exploration targets are delineated by multiplying the weights with the respective fuzzy normalised geochemical anomaly map of the gold pathfinder elements and integrating the weighted geochemical evidential layers using fuzzy Gamma operator. The reliability of the outcome was assured by the coincidence of known gold mineralisation with the potential regions delineated in the final gold potential map. The areas with high potential are good targets for detailed exploration of gold in the region. The results highlight that (a) application C-A fractal model can effectively separate geochemical anomalies as it considers the spatial variation of the data unlike the conventional statistical methods which account only the frequency of the data, (b) the FAHP allows more flexibility in judgements of multiple decision makers in a group and reduces the inconsistency of the result which is essentially required in the knowledge-driven predictive mapping of mineral prospectivity.

The Jiaojia-type gold deposits, hosting >80% of gold resource in the Jiaodong Peninsula, Eastern China, are characterized by veinlet- and disseminated-style mineralization associated with the Mesozoic detachment faults. In this study, we performed multi-constraint geological modeling and spatial analysis involving 3D buffer analysis, shape analysis, and field analysis for the Dayingezhuang gold deposit to quantitatively assess the gold distribution and its association with geological features. The obtained spatial data were further integrated into three dimensional (3D) prospectivity modeling by fuzzy weights-of-evidence (WofE) and continuous WofE methods to evaluate mineral potential. Our results determine a quantitative correlation between phyllic alteration thickness and tectono-geochemical anomaly to construct the geometric models of alteration zone. The hydrothermal intensity extracted from the models shows a bimodal distribution and it is significantly high in the center of No. 2 orebodies, indicating an overprinting gold mineralization. The spatial analysis on the Zhaoping fault reveals that the most probable locations for gold deposition were determined to be in segments of the Zhaoping fault with a slope of 20° to 40°, dip angle changes of −5°, and undulation of near 0 m. All of the features likely result from structural controls on fluid flow and infiltration, as well as variations in the stability of Au-bearing complexes related to fault morphology. 3D prospectivity models generated by continuous transformed spatial evidence values with lower bias and uncertainty yielded a higher predictive efficiency than classified evidential layers. Our study not only highlights that gold enrichment of the Jiaojia-type deposit is essentially controlled by shape features of detachment faults, but also emphasizes the applicability of 3D prospectivity modeling in identifying potential gold mineralization at depth in the Jiaodong Peninsula.

This study investigates the prospectivity for porphyry Cu mineralisation of the early Paleozoic Loch Lilly-Kars Belt, which follows the southern margin of the Paleoproterozoic Broken Hill (...

Multi-element geochemical surveys of rocks, soils, stream/lake/floodplain sediments and regolith are typically carried out at continental, regional and local scales. The chemistry of these materials is defined by their primary mineral assemblages and their subsequent modification by comminution and weathering. Modern geochemical datasets represent a multi-dimensional geochemical space that can be studied using multivariate statistical methods from which patterns reflecting geochemical/geological processes are described (process discovery). These patterns form the basis from which probabilistic predictive maps are created (process validation). Processing geochemical survey data requires a systematic approach to effectively interpret the multi-dimensional data in a meaningful way. Problems that are typically associated with geochemical data include closure, missing values, censoring, merging, levelling different datasets and adequate spatial sample design. Recent developments in advanced multivariate analytics, geospatial analysis and mapping provide an effective framework to analyse and interpret geochemical datasets. Geochemical and geological processes can often be recognized through the use of data discovery procedures such as the application of principal component analysis. Classification and predictive procedures can be used to confirm lithological variability, alteration and mineralization. Geochemical survey data of lake/till sediments from Canada and of floodplain sediments from Australia show that predictive maps of bedrock and regolith processes can be generated. Upscaling a multivariate statistics-based prospectivity analysis for arc-related Cu–Au mineralization from a regional survey in the southern Thomson Orogen in Australia to the continental scale, reveals a number of regions with a similar (or stronger) multivariate response and hence potentially similar (or higher) mineral potential throughout Australia. Thematic collection: This article is part of the Exploration 17 collection available at: https://www.lyellcollection.org/cc/exploration-17

The majority of machine learning algorithms that have been applied in data-driven predictive mapping of mineral prospectivity require a sufficient number of training samples (known mineral deposits) to obtain results with high performance and reliability. Semi-supervised learning can take advantage of the huge amount of unlabeled data to benefit the supervised learning tasks and hence provide a suitable scheme for mapping mineral prospectivity in cases where only few known mineral deposits are available. Semi-supervised random forest was used in this study to map mineral prospectivity in the southwestern Fujian metallogenic belt of China, where there is still excellent potential for mineral exploration due to the large proportion of areas covered by forest. The findings obtained from the current study include: (1) semi-supervised learning can make use of both the labeled and unlabeled samples to help improve the performance of mapping mineral prospectivity; (2) multi-dimensional scaling can be used to explore the clustering structure within the samples, which provides a tool to validate the usability of semi-supervised learning algorithms. In addition, the prospectivity map obtained in this study can be used to guide further mineral exploration in the southwestern Fujian of China.

One of the key objectives for mineral exploration is to map the most prospective areas for the selected deposit type at a regional scale, subsequently narrowing these down to camp size target areas and finally to individual prospects that can be developed into economic mines. Exploration targeting, at all scales, is constrained by conceptual mineral system models and requires the integration and interpretation of a variety of geophysical, geological and geochemical data. Geographic information system (GIS) applications are commonly used to integrate spatially referenced data sets to produce mineral prospectivity maps in support of the mineral exploration targeting process. A knowledge-driven fuzzy logic method is used in this work for data integration and prospectivity modelling for orogenic gold deposits in northern Finland. The modelling workflow is done in a stepwise manner at three scales. Each step simulates the successive stage used in mineral exploration from selecting the most prospective belt scale domain to the camp size target area and individual prospects. New, higher resolution data sets are added at each stage as is typically the case in mineral exploration. Northern Finland was selected as the test area due to the regional scale coverage of publicly available geoscientific data and its proven orogenic gold potential. The regional scale prospectivity map outlines the central part of the Paleoproterozoic Central Lapland Greenstone belt (CLGB) as the most prospective area for orogenic gold in northern Finland. The belt scale model of the CLGB improves the resolution mapping of several camp sized high prospectivity target areas. The camp scale model of one of these, a past producing mining camp, further narrows down the high prospectivity zones into prospect size targets. Three models have score Area Under Curve (AUC) values between 0.839 and 0.930 from the Receiver Operating Characteristic (ROC) method validation technique, indicating that the models are robust. The stepwise approach presented confirms that prospectivity mapping using the knowledge-driven fuzzy logic method is a scalable, flexible and relatively fast method which can be used for decision making at different stages of the exploration targeting process. Adding new data and rerunning the prospectivity model with the new data is fast and easy. The most important conclusion from the study is that remodelling individual regional and camp scale domains separately is highly recommended for mapping the most prospective target areas even if the data sets used are the same.

The identification of additional hydrocarbon reserves to buffer pressure from increasing energy demands in Nigeria necessitated the application of sequence stratigraphic framework in the analysis of sedimentary successions in the central portion of southern Anambra basin. Wireline logs and biofacies data from three exploratory wells (S-1, S-2 and S-3) were used for the study. The stratigraphy of the study area observed from the three wells showed an overall regressive succession with short-lived transgressions. Lithofacies associations deduced from well log signatures defined four lithostratigraphic units: The Mamu and Ajali formations of kate Maastrichtian age; the Nsukka Formation of kate Maastrichtian — Danian age and Imo Formation of Paleocene age. The study further used the depositional sequence model to identify sequences and accompanying systems tracts that are bounded at the top and bottom by unconformities. Dating of identified key stratigraphic surfaces was achieved by correlating chronostratigraphic biofacies data to third order cycle charts. Correlation across the three wells highlighted spatial distributions of reservoirs and some useful stratigraphic and structural discontinuities that could form hydrocarbon traps. It also showed profitable stratigraphic surfaces that would aid basin-wide correlation for improved horizon(s) mapping and hydrocarbon prospectivity in the Anambra basin. Erosional unconformities identified in the wells that correlated with major drops in global sea level, fingerprinted the influence of eustacy on sedimentation and sequence development. Other factors such as subsidence and sediment supply have direct relationships to the identified structures (faults) which also initiated the accommodation created for sedimentation in the study area.

A 3D mineral potential model was developed for the Tampia Gold Project in Western Australia to help constrain resource estimation, understand the distribution of gold grades from the resource estimation techniques with respect to geological and physiochemical continuity, and predict the location of new gold mineralisation for future exploration drilling to expand the gold resource at Tampia. The 3D mineral potential model was generated using predictive maps based on a local granulite-facies orogenic gold mineral system model. These were generated from regional scale data and data collected during a 40 m by 40 m resource drilling programme, and included lithology, structure, rock property data and geochemical data. The predictive capacity of each map was tested for the spatial correlation with training data from high grade gold drill intersections, using the weights of evidence technique. There were 44 predictive maps created that can be used as proxies to map the physical and chemical processes active in the orogenic mineral system at Tampia. Of these, 11 were chosen for the final model that had the highest spatial correlation with the training data and did not duplicate map patterns. A closely spaced infill drilling programme was subsequently undertaken over an area where the post probability results indicated high and continuous probability for gold mineralisation, while the resource model estimated less continuous and lower grade gold mineralisation. This infill drilling aimed to compare the gold continuity at a 10 m by 10 m drill spacing with the resource estimate gold grades and post probability distribution developed from 40 m by 40 m spaced resource drilling. The results from the 10 m by 10 m spaced drilling were thereby used to test the performance of both the resource and prospectivity models, and assess the utility of mineral potential modelling for use in developing geological domains to constrain resource estimation. Results from the first phase of infill drilling, which only covers 4% of the total model area, confirm the continuity of the post probability values and suggests that the mineral potential model predicts the location and distribution of gold mineralisation within the area drilled. The results were also better and more continuous than predicted by the resource estimate. Importantly, these results confirm that geological and physiochemical controls on gold mineralisation can be numerically measured and mapped at the scale of an orebody. This allows mineral potential modelling to be considered as an option to constrain and help inform the results of geostatistical techniques used in resource estimation.

Predictive modelling of mineral prospectivity using GIS is a valid and progressively more accepted tool for delineating reproducible mineral exploration targets. In this study, machine learning methods, including support vector machine (SVM), artificial neural networks (ANN) and random forest (RF), were employed to conduct GIS-based mineral prospectivity mapping of the Tongling ore district, eastern China. The mineral systems approach was used to translate our understanding of the skarn Cu mineral system into mappable exploration criteria, resulting in 12 predictor maps that represent source, transport, physical trap and chemical deposition processes critical for ore formation. Predictive SVM, ANN and RF models were trained by way of predictor maps, and corroborated using a 10-fold cross-validation. The overall performance of the resulting predictive models was assessed in both training and test datasets using a confusion matrix, set of statistical measurements, receiver operating characteristic curve, and success-rate curve. The assessment results indicate that the three machine learning models presented in this study achieved satisfactory performance levels characterized by high predictive accuracy. In addition, all models exhibited well interpretability that provided consistent ranking information about the relative importance of the evidential features contributing to the final predictions. In comparison, the RF model outperformed the SVM and ANN models, having achieved greater consistency with respect to variations in the model parameters and better predictive accuracy. Importantly, the RF model exhibited the highest predictive efficiency capturing most of the known deposits within the smallest prospective tracts. The above results suggest that the RF model is the most appropriate model for Cu potential mapping in the Tongling ore district, and, therefore, was used to generate a prospectivity map containing very-high, high, moderate, and low potential areas in support of follow-up exploration. The prospective areas delineated in this map occupy 13.97% of the study area and capture 80.95% of the known deposits. The fact that two newly discovered deposits occur within the prospective areas predicted by the prospectivity model indicates that the model is robust and effective regarding exploration target generation.

The spatial-temporal relationship between high-quality source rocks and reservoirs is a key factor when evaluating the formation, occurrence, and prospectivity of tight oil and gas reservoirs. In this study, we analyze the fundamental oil and gas accumulation processes occurring in the Songliao Basin, contrasting tight oil sand reservoirs in the south with tight gas sand reservoirs in the north. This is done using geochemical data, constant-rate and conventional mercury injection experiments, and fluid inclusion analyses. Our results demonstrate that as far as fluid mobility is concerned, the expulsion center coincides with the overpressure zone, and its boundary limits the occurrence of tight oil and gas accumulations. In addition, the lower permeability limit of high-quality reservoirs, controlled by pore-throat structures, is 0.1 × 10−3 μm2 in the fourth member of the Lower Cretaceous Quantou Formation (K1q4) in the southern Songliao Basin, and 0.05 × 10−3 μm2 in the Lower Cretaceous Shahezi Formation (K1sh) in the northern Songliao Basin. Furthermore, the results indicate that the formation of tight oil and gas reservoirs requires the densification of reservoirs prior to the main phase of hydrocarbon expulsion from the source rocks. Reservoir “sweet spots” develop at the intersection of high-quality source rocks (with high pore pressure) and reservoirs (with high permeability). Keywords: Spatial-temporal coupling, High-quality source rock, High-quality sandstone, Tight sandstone reservoir, Songliao Basin

In order to comprehensively utilize regional-scale geological, geochemical and geophysical datasets for future exploration of undiscovered porphyry Cu–Mo polymetallic deposits (PCMPDs) in the Chinese Eastern Tianshan orogenic belt, three data-driven mineral prospectivity mapping (MPM) methods, namely ordinary weights of evidence (WofE), fuzzy weights of evidence (FWofE) and logistic regression (LR), were employed to integrate these datasets for mapping prospectivity of undiscovered PCMPDs. Firstly, the geological setting and mineralization of PCMPDs in the Eastern Tianshan district are reviewed. Then, spatial datasets based on geological maps, stream sediment geochemical data, and Bouguer gravity and aeromagnetic data are introduced, and on the basis of the prospecting model for PCMPDs, layers of structural, lithological, geophysical and geochemical evidences are constructed using the spatial datasets by means of GIS-based techniques. Finally, these evidential layers were integrated by using the WofE, FWofE and LR methods to obtain posterior probability maps of PCMPDs and the results are critically compared. The main conclusions are that: (1) the porphyry Cu–Mo mineralization in the Eastern Tianshan was occurred in the subduction boundary of the Late Paleozoic Dananhu-Dacaotan arc system of Kanguertag-Huangshan deep fault belt. This geological inference is supported by all the data-driven MPM methods; (2) the conditional independence assumption for both WofE and FWofE can be easily violated in practical applications. This issue seems very difficult to be circumvented due to geological correlations of evidence layers; (3) the uncertainty of the LR modeling approach particularly with respect to models using multiclass response variables mainly arises from over-fitting of the (ln-transformed) linear relationship; and (4) if there is no need for estimation of the number of undiscovered PCMPDs, the prospectivity map biasedly estimated by either WofE or FWofE modeling can be recommended for targeting new exploration areas with more detailed reconnaissance of potential undiscovered PCMPDs. Otherwise, the prospectivity map unbiasedly estimated by LR modeling with binary evidence modeling approach can be priority of use.

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.

We propose to increase the role of empirical methods in mineral prospectivity modelling for two reasons: 1) to make use of data more effectively and 2) to decrease the effect of subjectivity included in expert interpretation. We present two approaches for using known mineral occurrences to define the relationship between observed or measured geoscientific parameters and the occurrence of mineralizations. In the first approach, we define the fuzzy memberships of each geoscientific parameter separately for fuzzy logic modelling. Our approach proves to be highly useful for investigating the quality of the data in addition to defining the membership transformation functions. In our test case, the data are somewhat scattered due to the inherent variability of ore-forming environments, and manual evaluation was required to guide the computations. For the second approach, we present a technique for delineating non-prospective regions to be able to focus more detailed prospectivity modelling to potentially prospective regions. Our study not only highlights the advantages of using computational methods in prospectivity modelling, but also emphasizes the important role of geological expertise in the modelling process.

Despite many decades of development, spatial prospectivity modelling is not yet widely used or accepted throughout the global mineral exploration industry. A common criticism of the method is that it is not practically useful because it has a bias to mature, well-known areas and generates excessively large areas of high-prospectivity. It is suggested that the reason for this is not primarily related to limitations in the prospectivity mapping algorithms but rather to issues relating to the use of input data sets. Specifically, it is common that the input data (such as geological interpretations) do not uniformly and objectively represent the search space of interest, omit critical targeting-relevant geoscientific elements (such as major, deep-seated ore-controlling structures) and have a large degree of unrecognised dependence. It is considered that these problems are not in principle barriers to the eventual successful deployment of this technology. However, future approaches to spatial prospectivity modelling need to explicitly address these concerns. It is suggested that the most effective method may be a hybrid of subjective human geological interpretation and objective, machine-based analysis, that captures the best aspects of these alternative approaches; i.e., an intelligence amplification (IA) rather than an artificial intelligence (AI) approach. A roadmap is proposed for improving the effectiveness of spatial prospectivity modelling that has implications for the broader community interested in mineral exploration targeting.

The targeting and discovery of epithermal porphyry mineral deposits can be enhanced using a structured quantitative methodology to analyse the distribution of ore deposits and model their spatial association with exploration evidence providing improved understanding on the controls of ore deposition. A novel exploration tool integrating field and ASTER SWIR and TIR satellite imagery has been developed which provides an enhanced means of resolving surface expressions of the residual silica core of the lithocap. The alteration zones were clearly resolved by the remote sensing data and an intimate spatial relationship between high-grade altered rocks and topographic highs was identified at a number of locations. A Mineral Prospectivity Modelling (MPM) approach, parameterized by the results of the remote sensing study, using a GIS-based weighted linear combination implementation of a Multi-Criteria Evaluation approach and utilising a fuzzy Analytical Hierarchy Process to elucidate expert knowledge has been implemented to target high sulfidation epithermal porphyry deposits on the Island of Lesvos, Greece. The results from this integrated altitudinal-compositional modelling approach closely matched the hydrothermal alteration mapped in the field supporting the accuracy of this MPM approach.

The Goldwyer Formation of the Canning Basin has been regarded as a highly prospective shale play. This study assesses the potential prospectivity of this source rock as an unconventional hydrocarbon resource. Considering the sparsity of wells penetrating the Middle Ordovician Goldwyer across the vast under-explored area of the Canning Basin, a basin-wide study of the source rock is not warranted. Goldwyer assessment of the Barbwire Terrace, a subdivision of the Canning Basin, is carried out instead.This assessment includes the estimation of key shale play properties, such as, total organic carbon, total porosity, water saturation, and brittleness index. Each property was estimated from available well data by testing multiple estimation methods. TOC values were derived from multiple regressions of different well data. A simplified Archie's equation was used to estimate water saturation. Density porosity method was primarily used for total porosity estimations. Sonic data along with density were utilized to estimate brittleness index.Each property was then modelled across the Goldwyer Formation within the terrace. This provided geostatistical estimates on the propagation of such properties. In order to generate sweet spot maps for the Barbwire Terrace, averaged maps of different properties were combined in a weighted manner. This approach attempts to simplify the complexity of unconventional resource assessment, which therefore has provided a single product evaluating the prospectivity of the Goldwyer as a hydrocarbon resource.Results have shown that TOC and porosity are mostly the deciding factors for the prospectivity of this source rock, given that their values can be too small where the Goldwyer is deemed non-prospective. Nonetheless, sweet-spot maps show that most prospective zone is the Upper Goldwyer (Goldwyer I), followed by the upper parts of the Lower Goldwyer (Goldwyer III). More specifically, southern flanks of north-western and middle regions of the Barbwire Terrace tend to be more prospective. A stricter approach where cut-off values were applied for each property showed that sweet-spot maps are only prospective in the southern flanks of the middle Barbwire Terrace of Goldwyer I. Keywords: Sweet-spot maps, Formation evaluation, Petrophysical modelling

The Canning Basin is a largely unexposed and underexplored frontier basin, formed mostly in the Paleozoic. Geological knowledge of this basin is based predominantly on sparse regional “vintage” two-dimensional seismic and small three-dimensional (3-D) seismic surveys and less than 230 exploration wells. Following seismic interpretation, an integrated interpretation was completed on airborne gravity gradiometer (AGG), magnetic, seismic, well, and complementary data along the southwestern margin of the Fitzroy trough and Gregory subbasin. Seismic data were reinterpreted using AGG data to produce a better constrained geological model. A basement structure map, two intrasedimentary structure maps, and a formation distribution map were produced. The interpretation of seismic profiles, validated through 2.5-dimensional gravity gradiometer modeling, is essential to this workflow. Repeatedly reactivated west–northwest and northwest structural trends, inherited from Proterozoic orogenies, respectively delineate the Fitzroy trough and the Gregory subbasin with its northwestern structural extension into the Fitzroy trough, the Gregory subbasin trend. Subsidence occurred during two periods of extension. An asymmetric extensional system of the Fitzroy trough controlled Ordovician–Silurian deposition of the Carribuddy Group. Devonian–Carboniferous subsidence defines the Gregory subbasin trend. This Pillara extension reactivated structures in the east of the Fitzroy trough. Simultaneous activity of both extensional fault systems and growth faulting controlled the facies and thickness distribution of carbonates and clastics of the early Carboniferous Fairfield Group. The Meda and Fitzroy transpressional phases inverted faults of the Gregory subbasin trend and Fitzroy trough, producing prospects by structural interference. The improved understanding of tectono-stratigraphic relationships, including the 3-D distribution of carbonate reservoirs, benefited the planning of seismic surveys, prospect evaluation, drilling, and acreage relinquishment.

The Yueshan orefield is one of the best-known Fe–Cu orefields in China and is located within the central Yangtze metallogenic belt ofin Anhui Province, China. The recent discovery of the skarn-type Zhuchong Fe–Cu deposit at depth within the orefield has highlighted the prospectivity of this area to host deep-seated skarn-type mineralization. However, the effectiveness of traditional geophysical exploration techniques is reduced with increasing depth. This, combined with recent developments in 3D mineral prospectivity modeling has led to the use of a 3D targeting approach for exploration for deep-seated and concealed mineralization in this area. However, to date the usefulness of this approach has been limited by a lack of an approach that could generate more useful 3D predictive maps. This study presents a 3D computational simulation based mineral prospectivity modeling approach that identified several exploration targets for concealed and deep-seated skarn-type mineralization within the Yueshan orefield. These prospective targets include areas of known mineralization as well as a number of new targets for future mineral exploration. In addition, the analysis of the resulting data using a capture-efficiency curve indicates that these 3D computational simulation approaches can provide additional predictive information for mineral exploration, indicating that 3D computational simulation should have a key role in the development and use of future 3D prospectivity modeling techniques during exploration targeting.

Seismic interpretation, geological mapping and depth conversion of the Zechstein Supergroup (Z2 cycle), using high-quality well-calibrated three-dimensional (3-D) seismic, has revealed the complex palaeomorphology of a deeply-buried ancient carbonate shelf-margin on the southwestern margin of the Southern Permian Basin. The new mapping shows that the margin comprises a series of corrugated embayments and promontories which extend up to 5 km (3 mi) into the basin and locally have a palaeobathymetric relief of >200 m (650 ft). Well calibration across the margin demonstrates a strongly bipartite lateral thickness distribution between a high velocity anhydrite-dominated shelf and a lower velocity halite-dominated basin. Strong lateral and vertical velocity variations in the Upper Permian Zechstein Supergroup are known to have major impacts upon seismic imaging and depth conversion in the SPB. The resulting uncertainty remains one of the major challenges when interpreting and assessing the prospectivity of the underlying Upper Permian, Rotliegend Group (Leman Sandstone Formation) reservoirs in the UK Southern North Sea. An understanding of the Zechstein shelf edge's 3-D physiography and its velocity variation has implications for the delineation of traps containing the prospective reservoirs that lie below. Recognition of the complexity and effects of this shelf-margin contributed to the recent Juliet Discovery whose position outside of the presently defined Rotliegend Group play fairway suggests that prospectivity is more extensive along the basin margin than previously thought. As such, the work provides a means by which to identify and delineate new structures and extend the life of this mature yet prolific gas province.

Gas chimneys are geological features that are associated with seal bypass systems. Knowledge of the capacity of the seal is important for the evaluation of the petroleum systems and Carbon Capture and Storage (CCS) studies. The seal is breached when the capillary pressure threshold is exceeded by the underlying buoyancy pressure. The chaotic seismic character that is generally associated with the gas chimneys has been studied previously to evaluate the origin and geometry of the chimneys. Seismic attributes have also been used to determine the character of the gas chimneys and the potential association of hydrocarbon fields. However, it is important to evaluate the sealing potential of the cap rock in the vicinity of the gas chimney. This paper demonstrates that the chimney can be used as a calibration point and, with the use of seismic attributes, variations of sealing capacity can be evaluated. Out of many existing seismic attributes, understanding the shale structure is crucial to narrow the search criteria for the right class of attributes. Using the most useful stream of seismic attributes, the average sealing potential of the Lakes Entrance Formation is estimated. The sealing capacity map across the field is then shown to help evaluate the prospectivity of the area of study and may upgrade subsequent play fairway maps.

We have used visible near infrared (VNIR) and shortwave infrared (SWIR) spectral bands of Advanced spaceborne thermal emission and reflectance radiometer (ASTER) to identify prospective zones of mineralization associated with the gold deposit in Bhukia area, Rajasthan. Lineament density, the extent of influence of regional structures/lineament, updated lithological map and the reference magnetic anomaly map are used for prospectivity mapping based on Fuzzy-logic based gamma overlay analysis. We have used high pass filtered (5 × 5 windows) image of selected bands of ASTER to delineate geological lineaments; which are believed to have a significant role in localizing hydrothermal fluid movement and also have influenced the ore deposition. In this regard, two evidential layers are prepared for geological structures. One layer is prepared to delineate a zone of influence of geological structures and other is on lineament density. For this, we also have used ground magnetic data and EIGEN6C4 model based Bouguer gravity data to delineate regional structures; which are combined with the ASTER-derived lineaments to derive composite lineament density map. Principle component image of ASTER bands, suitable false color composite (FCC) images of selected bands are derived to update litho-contacts of reference geological map to use as an evidential layer. Weightages for the themes, on favorable structure, lineament density, and lithology are assigned based on the geological understanding of their relative influence on the formation of the deposit. Weightage given to each layer is rescaled to the range of 0–1 using suitable fuzzy function. In addition to the above-mentioned geogenic factors, magnetic anomaly map is also used as an evidential layer. In this regard, empirical relation between a magnetic anomaly and gold sulphides is used as the reference to give gradational higher weightages to high magnetic anomaly zones; which are occurring within the spatial extent of the host rocks. Highly prospective zone in the Fuzzy prospective map prepared using above evidential layers is segmented for “potential or priority areas” and consequently validated. In this regard, high Zr/Hf anomaly map, Fe2O3 oxide anomaly map, “basement-highs” derived from EIGEN6C4 gravity model and ASTER-derived gossan exposure map are used to identify few priority areas within the highly prospective zone of prospect map. Gossan exposure map used in above purpose has been derived based on the implementation of the matched filtering method on ASTER image using gossan broadband spectra as a reference. Highly prospective zone identified using few regional evidence layers are validated using the localized evidences; which have been used to identify few priority areas within the highly prospective zones. Present approach of conjugate use of regional and local evidence layers to identify the priority areas within the highly prospective zone can be regarded as an innovative method to implement for gold exploration in any part of the world.

This study compares the use of joint singular value decomposition and semi-discrete decomposition (JSS) and non-negative matrix factorisation with univariate analysis of raw data, to detect multi-element patterns in soils related to geochemical dispersion from Mississippi Valley-Type Pb-Zn deposits in the Irankuh area of central Iran. Joint singular value decomposition and semi-discrete decomposition clustering of the data identified a suite of mineralisation-related variables and corresponding sample clusters that are spatially associated with mineralisation or variations in parent lithology . Non-negative matrix factorisation generated three main factors and sample clusters that relate to the main zones of sulphide mineralisation, variations in clay mineralogy and the composition of unaltered host rocks. These two matrix decomposition techniques deliver similar results, though JSS delivers a significantly higher sample classification accuracy. Both methods result in delineation of a contiguous cluster of samples above an extensive zone of blind mineralisation at the Tappe Sorkh Pb-Zn deposit and define new potentially mineralised targets between this deposit and the Gushfil Pb-Zn deposit. More conventional univariate methods to detect anomalous populations of Pb and Zn in the soil geochemical data, such as number-size fractal analysis, were less effective at defining these targets. Joint application of practical matrix decomposition techniques, utilising noise removal and modelling of the underlying geochemical patterns associated with ore-forming processes, has led to more reliable prospectivity mapping of the Pb-Zn deposits in Irankuh.

Assigning realistic weights to targeting criteria in order to synthesize various geo-spatial datasets is one of the most important challenging tasks for mineral prospectivity modeling (MPM). Techniques for multiple criteria decision-making (MCDM), like MPM, are deeply concerned with combining a large-scale exploration dataset into a single evaluation model for localizing prospects of a certain deposit type. In this paper, we develop the data-driven TOPSIS procedure, as a GIS-based MCDM technique for MPM. Because weighting and integrating various exploration evidence layers are influenced by intricacy and vagueness of ore mineralization process, imprecise selection of targeting criteria may reduce the possibility of exploration success. To address this problem, we applied prediction–area plot for prioritizing, recognizing and weighting efficient and inefficient targeting criteria. In addition, normalized density (Nd) index was then used for assigning significant weights to fractal-based discretized classes of each targeting criterion. After recognition of efficient and inefficient targeting criteria, data-driven TOPSIS procedure was adapted based on participation of only efficient targeting criteria as well as all targeting criteria for porphyry-Cu prospectivity in Varzaghan district, NW Iran. For quantitative assessment, a success rate curve for each of the two prospectivity models generated in this study was drawn. The results prove the superiority of the predictive model based on using efficient targeting criteria.

Rock Physics Modelling and Seismic Inversion were carried out in an Onshore Niger Delta Field for the purpose of characterizing a hydrocarbon reservoir. The aim of the study was to integrate rock physics models and seismic inversion to improve the characterization of a selected reservoir using well-log and 3D seismic data sets. Seven reservoir sands were delineated using suite of logs from three wells. In this study, the sand 4 reservoir was selected for analysis. The result of petrophysical evaluation shows that the sand 4 reservoir is relatively thick (62 ft) with low water saturation (0.33), shale volume (0.11) and high porosity (0.32). These results indicate reservoir of good quality and producibility. Cross-plot of property pairs (acoustic impedance (Ip) vs. lambda-rho (λρ) and mu-rho (μρ) vs. lambda-rho (λρ) color-coded with reservoir properties reveals three distinct probable zones: hydrocarbon sand, brine sand and shale. Results show that low Ip, λρ and μρ associated with hydrocarbon charged sands correspond to low Sw and Vsh and high &#216. The integration of rock physics models and inverted rock attributes effectively delineated and improved understanding of already producing reservoirs, as well as other hydrocarbon charged sands of low Sw, Vsh, and high &#216 to the east of existing well locations, which indicate possible by-passed hydrocarbon pays. The results of this work can assist in forecasting hydrocarbon prospectivity and lessen chances of drilling dry holes in MUN onshore Niger delta field.

The Darlot-Centenary gold deposit is one of the larger known mineralised systems in the southern end of the West Australian Yandal Greenstone Belt, with an estimated 2.7 Moz having been extracted from the Darlot Centenary Mine since 1988. The area is well explored near surface but given the proven endowment there is potential for significant additional mineralisation at depth. With current proven reserves dwindling, Gold Fields recognised the need to identify a technology to fast-track target generation in order to more rapidly evaluate the nearby rock volume. In August 2016 Gold Fields began investigating the potential for 3D reflection seismic to accelerate evaluation of the rock volume accessible via existing workings. In November 2016 a seismic crew was on ground acquiring approximately 150km3 of 3D seismic data (25km2 surface area x 6km depth). The survey coverage was designed to image the local steeply dipping geology and structures. Processing of the seismic dataset was completed in Q1 2017 and Gold Fields has completed preliminary interpretation of the 3D cube. The seismic data has provided a rich 3D picture of the Darlot structural framework to depth, which could not be obtained by any other geophysical method. It has highlighted a number of features with similar characteristics to known mineralisation and has provided a better defined structural framework that has greatly assisted the fundamental geological understanding and further aided ranking of these targets in terms of prospectivity.