To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets ...To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.展开更多
This study examines the molecular and isotopic composition of 193 gas samples collected from oil and gas fields across Colombia's onshore basins with active hydrocarbon production.Comparative analyses were conduct...This study examines the molecular and isotopic composition of 193 gas samples collected from oil and gas fields across Colombia's onshore basins with active hydrocarbon production.Comparative analyses were conducted on both isotopic and molecular compositions across the Lower Magdalena Basin(LMB),Middle Magdalena Basin(MMB),Upper Magdalena Basin(UMB),Putumayo Cagu an Basin(PUTCAB),Catatumbo Basin(CATB),Eastern Llanos Basin(LLAB),and Eastern Cordillera Basin(ECB).The primary objectives were to classify the gases produced,characterize their origins,assess transformation processes such as biodegradation and migration,and analyze the statistical distribution patterns of their components.This geochemical characterization aims to support the discovery of new reserves for both natural gas(NG)and liquefied petroleum gas(LPG),given Colombia's potential risk of diminished energy selfsufficiency in gas resources.The basins under study produce dry gas,wet gas,and liquefied petroleum gas(LPG/C_(3+)),all associated with oil and gas fields of commercial hydrocarbon production.Notably,the LLAB contains the highest proportions of heavy isotopic carbon and C_(3+)(LPG)concentrations,whereas LMB is characterized by isotopically lighter methane,indicative of dry gas predominance.Results suggest a predominantly thermogenic origin for the gases studied,generated within the oil and gas windows,with several samples originating from secondary oil cracking,while some samples from LMB display a likely biogenic origin.Additionally,evidence of gas migration and biodegradation was observed in a significant subset of samples.The analysis of statistical distributions and compositional trends reveals a prevalent high methane content,with substantial C_(2)-C_(5)(C_(2+))gas concentrations across all basins studied.This composition underscores the potential for both natural gas(NG)and LPG production.The C_(3+)(LPG)content varies between 1%and 92%,with 35%of the samples containing less than 5%LPG.High original gas-in-place(OGIP)volumes and substantial LPG content in the Eastern Llanos foothills,encompassing fields such as Cusiana and Cupiagua,highlight the prospective potential of this region.Near-field exploration could further add reserves of both NG and LPG.展开更多
While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geolo...While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.展开更多
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...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 3 D geological modeling,and 3 D quantitative prediction of deep resources.Taking the Lala copper mine in Sichuan Province as an example,this paper carries out deep 3 D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.展开更多
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 pro...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 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D 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 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D 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 3 D CNN is promising for 3 D MPM as it eliminates the interference factors.展开更多
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the ev...Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.展开更多
Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriat...Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets.The data-driven Random ForestTM algorithm is employed to model tungsten mineralisation in SW England using a range of geological,geochemical and geophysical evidence layers which include a depth to granite evidence layer.Two models are presented,one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step.The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model.The typically subjective approach is guided using the Receiver Operating Characteristics(ROC)curve tool where transformed data are compared to known training samples.The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model.The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets.Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known.The Confidence Metric,derived from model variance,is employed to further evaluate the models.The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation.The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables.Finally,legacy mining data,from drilling reports and mine descriptions,is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits.Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 m.In summary,the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling,while the newly derived Confidence Metric generates reliable mineral exploration targets.展开更多
Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with...Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained展开更多
The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has e...The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery.展开更多
BasinMod 1D software with faulting module is used to model two synthetic wells taken from a geoseismic section in Exploration Block 2 in western Nepal to understand the burial and thermal history of exterior belt(Tera...BasinMod 1D software with faulting module is used to model two synthetic wells taken from a geoseismic section in Exploration Block 2 in western Nepal to understand the burial and thermal history of exterior belt(Terai)and Siwalik fold and thrust belt.The study focuses the measured inputs of source and reservoir rocks of Surkhet Group consisting of Swat shale(2%),TOC and Melpani sandstone porosity(10%).The geohistory curves show rapid sedimentation and tectonic subsidence.The thermal history is constrained using a 20℃/km geothermal gradient for the exterior belt,whereas for the Siwalik fold and thrust belt,a two-step geothermal gradient is proposed using a 20℃/km for the upper 2,000 m and 23℃/km below this depth.The modeled values for maturity show that the Surkhet Group lies in the mid mature oil window in the exterior belt,but for the Siwalik fold and thrust belt,the hanging-wall Paleogene wedge is in the early mature stage,whereas the footwall Paleogene wedge is in the late mature stage.Oil generation for the Swat shales started at 6.3 Ma at 3,988 m depth with peak oil generation 2.4-1.3 Ma at 5,435-5,782 m depth in the exterior belt.However,the Siwalik fold and thrust belt modeling shows that the footwall Swat Formation has no oil generation capacity after the faulting episode,whereas it had been producing oil since about 8.5 Ma at 3,800 m with main phase ofoil generation at about 7 Ma at 4,600 m.The hanging-wall Swat Formation has been in the early mature stage of oil generation since faulting.The timing of structural trap formation window is set to 4.1-1.8 Ma based on geological evidence from the literature.The results show trap formation is more or less contemporaneous with hydrocarbon generation and expulsion and timing will be critical for assessments of the prospectivity.展开更多
The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-el...The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.展开更多
Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent co...Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent coverage of well-documented and good quality data enables testing of variable exploration modeling in an efficient way. The study area of this research is the most important part of Cu (Mo) porphyry—type mineralization belt in Iran. There are some well-known porphyry copper deposits in this region like Sarcheshmeh and Meiduk mines, but certainly there are same grounds to search for new porphyry deposits. The risks of developing mineral resources need to be known as accurately as possible, with regarding to all features those are effective in mineralization. These features can be recognized respect to Critical Genetic Factors (CGF’s) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF’s can be employed for designing a Conceptual Genetic Model (CGM). Evidence maps create on the basis of CGM and then integrate together for production of Mineral Prospectivity Map (MPM). This map categorizes the areas based on their exploration importance. There are several techniques for creation of MPM. Interval Valued Fuzzy Sets (IVFSs) TOPSIS method was applied in this research. This method as a knowledge-driven method, allocate appropriate weights to layers on the basis of the effective membership, non membership, and non-certainty. The fundamental concept of TOPSIS is that the chosen alternatives should have the shortest distance from the positive ideal points (A*) and the farthest distance from negative ideal points (A-).展开更多
Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration le...Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.展开更多
In this study,we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karo...In this study,we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province(Gondwana Supercontinent).Wedemonstrate that a variety of trace elements,including most of the lanthanides,chalcophile,lithophile,and siderophile elements,can be predicted with excellent accuracy.This finding reveals that there are reliable,high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks.Since the major and minor elements are used as predictors,prediction performance can be used as a direct proxy for geochemical anomalies.As such,our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations.Compared to multivariate compositional data analysis methods,the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies.Because we do not use multivariate compositional data analysis techniques(e.g.principal component analysis and combined use of major,minor and trace elements data),we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space.Therefore,we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data.The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping,or be used as a pre-processor to reduce the detection of false geochemical anomalies,particularly where the data is of variable quality.展开更多
Application of regional geophysical methods for hydrogeological purposes has increased over the last two decades especially in arid and semi-arid areas. A project to map the Kraaipan granite-greenstone terrain in sout...Application of regional geophysical methods for hydrogeological purposes has increased over the last two decades especially in arid and semi-arid areas. A project to map the Kraaipan granite-greenstone terrain in southeast Botswana has recently been undertaken using regional aeromagnetic and gravity data with the aim to map the rocks at depth to understand the geology while the secondary objective was to subsequently assess the mineralization and groundwater potential in the area. An integrated analysis of the aeromagnetic and gravity data and their derived/processed products is hereby investigated for groundwater for drinking and agricultural purposes. The studies include: subsurface characterisation and delineation of structural framework suitable for groundwater exploration and determination of petrophysical relationships used to link the geophysical properties (e.g., density) to hydrological properties (e.g., porosity). The results of interpretation indicate that the rocks are under ~50 m of Kalahari cover and the study area is composed of three aquifers: the extensive hard rock aquifer (granitic and volcanic), the important (fractured) karst aquifer and the minor sedimentary aquifer. The area is dissected by an ENE-to-EW-trending dyke swarm visible on the regional aeromagnetic data and much clearer on high resolution aeromagnetic data. Minor fault and/or dyke elements of NW-SE and NE-SW trend are observed. Spectral analysis reveals three main average ensample interfaces at depths of 0.7 km, 1.99 km and 4.8 km. The linear Euler solutions maps reveal that the majority depths to top of magnetic bodies range from 40 m to 400 m throughout the survey area. The shallowest depths are the most significant one in this case as they probably relate to depth of bedrock and thickness of regolith or thickest sediments. For 2695 existing boreholes analysed, maximum borehole depth is 482 m (mean 108 m), and almost half (1263) were dry with another 972 having low yield (1 - 5 m3/hr) and 432 yielding 6 - 49 m3/hr and only 28 above 50 m3/hr (maximum ~160 m3/hr) and an average water strike of 64 m. There is very little correlation between interpreted hydrogeological features and the existing borehole locations. The study shows the importance of preliminary geophysical investigations before ground borehole siting and drilling in order to improve borehole success rates and/or reduce costs inherent in groundwater projects.展开更多
The reason of this research is to identify the favorable areas for copper, zinc, and lead mineralization in the western part of the 1:100,000 Tafresh geological Sheet in the Urmia-Dokhtar structural zone of Iran. Effe...The reason of this research is to identify the favorable areas for copper, zinc, and lead mineralization in the western part of the 1:100,000 Tafresh geological Sheet in the Urmia-Dokhtar structural zone of Iran. Effective data layers for mineralization, such as geology, geochemistry, structures, and satellite images, were analyzed and then integrated using the AHP-OWA method to identify favorable areas. Geochemical stream samples were analyzed by univariate, multivariate, and classical statistical methods and revealed the first, second, and third class anomalies for copper, zinc, and lead in the study region. Detection of hydrothermal alteration zones by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery in various algorithms, such as Relative absorption Band Depth (RBD), Minimum Noise Fraction (MNF), and Least Square Fit (LS-Fit), shows that argillic, phyllic, propylitic, and iron oxide alterations develop around the faults in the area under study. The favorable areas for copper, zinc, and lead mineralization have been identified by a combination of evidence maps of lithology, faults, dikes, geochemistry, and alteration data layers. Field observations in the area under study have confirmed the results.展开更多
The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zi...The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zinc. The Synthesis process is done by the Analysis Hierarchy Process (AHP) and Index Overlay (IO) methods. Of previous studies on the area, various companies providing Geological maps and in particular the company of Jiangxi providing its own geochemical maps can be mentioned. The reasons for doing this research and its innovation in Kalatereshm’s sheet can be justified as to be valuable and the fact that we would be able to save in time and cost by doing so. Previous case studies on this particular region lacked the necessary use of an advanced software and method. The informational layers included geochemical layers (the second and first ratings were given to Copper and Lead respectively by weighting based on AHP method), geology layer (the fourth and second ratings were given to Copper and Lead respectively by weighing based on AHP method), fault layer (the first and fourth ratings were given to Copper and Lead respectively by weighting based on AHP method), satellite imagery layer (the third rating was given to both Copper and Lead by weighting based on AHP method) and the more applicable areas for field exploration and detailed procedures of exploration had been determined (the mentioned ratings were delineated by each element’s respective weight in each layer and their importance in the Synthesis of informational layers).展开更多
This paper evaluates the hydrocarbon prospectivity and play risks of “Bob” field in Niger Delta Basin, Nigeria. The aim is to enhance exploration success through improved approach/technique by incorporating risk ana...This paper evaluates the hydrocarbon prospectivity and play risks of “Bob” field in Niger Delta Basin, Nigeria. The aim is to enhance exploration success through improved approach/technique by incorporating risk analysis that previous studies have not fully considered. This approach combines a set of analyses including stratigraphic/structural, amplitude, petrophysical parameter, volumetric and play risk using a suite of well logs and 3D seismic data. Maximum amplitude anomaly map extracted on the surfaces of delineated 3 reservoirs revealed 6 prospects, namely: Dippers, Cranes, Turacos, Nicators, Jacanas and Pelicans with hydrocarbon accumulation. Petrophysical analysis showed ranges of values for porosity, permeability and water saturation of 0.21 to 0.23, 158.96 to 882.39 mD, and 0.07 to 0.11, respectively. The various prospects yielded the following stock tank volumes 12.73, 6.84, 3.84, 11.32, 7.42 and 4.76 Million barrels (Mbls) each respectively in a column of 66 ft reservoir sand in the study area. Play risk analysis results gave: Pelicans and Nicators (low), Turacos and Dippers (moderate), while Jacanas and Cranes show high risk with minimal promise for good oil accumulation. The prospects possess good reservoir petrophysical properties with low to moderate risk, thus, viable for commercial hydrocarbon production, which increases confidence in management decisions for production.展开更多
Mineral exploration campaigns are financially risky.Several state-of-the-art methods have been developed to mitigate the risk,including predictive modelling of mineral prospectivity using principal component analysis(...Mineral exploration campaigns are financially risky.Several state-of-the-art methods have been developed to mitigate the risk,including predictive modelling of mineral prospectivity using principal component analysis(PCA)and geographic information systems(GIS).The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets.However,some of its limitations are the dependence on sample stoichiometry(e.g.,the existence of minerals),the necessity of log-ratio transformations when dealing with compositional data,and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping.In this study,we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML.We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province(Quebec and Labrador),Canada.The region is known for its REEs endowment and the data were gathered for prospectivity mapping.A comparison with the established multivariate hybrid data-and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort,our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications.The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region.These findings have potentially wider implications on exploration target generation,where project risks(financial,environmental,political,etc.)and geochemical anomalies must be quantified using robust and effective datadriven approaches.In addition,our methodology is more replicable and objective,as manual geoscientific interpretation is not required during the detection of geochemical anomalies.展开更多
Bangladesh is a country with limited sources of energy and since 1990 natural gas has been its main source of energy. Most of the exploration approaches had been conducted onshore especially in the central and eastern...Bangladesh is a country with limited sources of energy and since 1990 natural gas has been its main source of energy. Most of the exploration approaches had been conducted onshore especially in the central and eastern part of Bangladesh, particularly in northeastern Sylhet basin. Among the hydrocarbon provinces, the East Delta Hill Tract province is an under explored petroleum province in Bangladesh. An exploratory well drilled in Sitakund anticline was found dry but no reasonable cause was perceived why that well went dry. Although many works had been carried out in Chittagong Hill Tracts but none of them was cumulative and descriptive. In this study, the overall hydrocarbon prospect of the Chittagong Hill Tracts was analyzed by mapping of potential zones on the basis of the evaluation of regional structure and construction of lithocolumn of the prospective zones. The five elements of the petroleum system discussed thoroughly to find overall petroleum prospect of the study area. Source rocks of Chittagong Hill Tracts are mainly Bhuban shale, reservoir rock is sandstone from Bhuban-Bokabil formation, the way of migration path is both through longitudinal and cross fault. The data of source rock and seal is collected from previous researches. Multiple types of traps have been found there. Conventional anticlinal traps which are highly disturbed due to tectonic instability & the core part are shale diapirism. Most of the anticlines are plunging and the nose or plunge area might be prospective to HC for being comparatively less faulted. Broad synclinal areas between tight narrow anticlines are another prospective area for HC. These similar types of synclines are also found in Tripura, India which is a highly prospective area for petroleum and their small anticlinal hums within syncline are also prospective. Some stratigraphic traps have also been found in Tripura from where production has been started already. Both Chittagong Hill Tracts and Tripura Fold Belts are parts of great Arakan Fold Belts, so similar type of structures might be prospective here. Apart from these, Bangladesh is a deltaic country. So stratigraphic trapment like channel sand, pinch-outs is possible. Considering all the elements of petroleum prospectivity of the area and factors discussed above, it is quite clear that Chittagong hill tracts might be the next target for HC exploration program.展开更多
基金financially supported by the Ministry of Science and Technology of China(Nos.2022YFF0801201,2021YFC2900300)the National Natural Science Foundation of China(Nos.41872245,U1911202)the Guangdong Basic and Applied Basic Research Foundation(No.2020A1515010666)。
文摘To comprehensively utilize the valuable geological map,exploration profile,borehole,and geochemical logging data and the knowledge on the formation of the Jinshan Ag-Au deposit for forecasting the exploration targets of concealed ore bodies,three-dimensional Mineral Prospectivity Modeling(MPM)of the deposit has been conducted using the weights-of-evidence(WofE)method.Conditional independence between evidence layers was tested,and the outline results using the prediction-volume(P-V)and Student's t-statistic methods for delineating favorable mineralization areas from continuous posterior probability map were critically compared.Four exploration targets delineated ultimately by the Student's t-statistic method for the discovery of minable ore bodies in each of the target areas were discussed in detail.The main conclusions include:(1)three-dimensional modeling of a deposit using multi-source reconnaissance data is useful for MPM in interpreting their relationships with known ore bodies;(2)WofE modeling can be used as a straightforward tool for integrating deposit model and reconnaissance data in MPM;(3)the Student's t-statistic method is more applicable in binarizing the continuous prospectivity map for exploration targeting than the PV approach;and(4)two target areas within high potential to find undiscovered ore bodies were diagnosed to guide future near-mine exploration activities of the Jinshan deposit.
文摘This study examines the molecular and isotopic composition of 193 gas samples collected from oil and gas fields across Colombia's onshore basins with active hydrocarbon production.Comparative analyses were conducted on both isotopic and molecular compositions across the Lower Magdalena Basin(LMB),Middle Magdalena Basin(MMB),Upper Magdalena Basin(UMB),Putumayo Cagu an Basin(PUTCAB),Catatumbo Basin(CATB),Eastern Llanos Basin(LLAB),and Eastern Cordillera Basin(ECB).The primary objectives were to classify the gases produced,characterize their origins,assess transformation processes such as biodegradation and migration,and analyze the statistical distribution patterns of their components.This geochemical characterization aims to support the discovery of new reserves for both natural gas(NG)and liquefied petroleum gas(LPG),given Colombia's potential risk of diminished energy selfsufficiency in gas resources.The basins under study produce dry gas,wet gas,and liquefied petroleum gas(LPG/C_(3+)),all associated with oil and gas fields of commercial hydrocarbon production.Notably,the LLAB contains the highest proportions of heavy isotopic carbon and C_(3+)(LPG)concentrations,whereas LMB is characterized by isotopically lighter methane,indicative of dry gas predominance.Results suggest a predominantly thermogenic origin for the gases studied,generated within the oil and gas windows,with several samples originating from secondary oil cracking,while some samples from LMB display a likely biogenic origin.Additionally,evidence of gas migration and biodegradation was observed in a significant subset of samples.The analysis of statistical distributions and compositional trends reveals a prevalent high methane content,with substantial C_(2)-C_(5)(C_(2+))gas concentrations across all basins studied.This composition underscores the potential for both natural gas(NG)and LPG production.The C_(3+)(LPG)content varies between 1%and 92%,with 35%of the samples containing less than 5%LPG.High original gas-in-place(OGIP)volumes and substantial LPG content in the Eastern Llanos foothills,encompassing fields such as Cusiana and Cupiagua,highlight the prospective potential of this region.Near-field exploration could further add reserves of both NG and LPG.
基金Project(2017YFC0601503)supported by the National Key R&D Program of ChinaProjects(41772349,41972309,41472301,41772348)supported by the National Natural Science Foundation of China。
文摘While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.
基金financially supported by the National Natural Science Foundation of China(No.42002298)the National Key Research and Development Program of China(No.2017YFC0601501)+1 种基金China Geological Survey(No.DD20201181)the Open Research Fund Program of the Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring(Central South University),Ministry of Education(No.2020YSJS09)。
文摘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 3 D geological modeling,and 3 D quantitative prediction of deep resources.Taking the Lala copper mine in Sichuan Province as an example,this paper carries out deep 3 D quantitative prediction of mineral resources and makes a positive contribution to the future prediction and evaluation of mineral resources.
基金financially supported by the Chinese MOST project“Methods and Models for Quantitative Prediction of Deep Metallogenic Geological Anomalies”(No.2017YFC0601502)and“Research on key technology of mineral prediction based on geological big data analysis”(No.6142A01190104)。
文摘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 3 D MPM.In this study,taking the Huayuan sedimentary Mn deposit in Hunan Province as an example,we construct a 3 D digital model of this deposit based on the prospectivity model of the study area.In this approach,3 D predictor layers are converted from the conceptual model and employed in a 3 D convolutional neural network(3 D CNN).The characteristics of the spatial distribution are extracted by the 3 D CNN.Subsequently,we divide the 22 extracted ore-controlling variables into six groups for contrast experiments based on various combinations and further apply the 3 D 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 3 D CNN model performs well in the 3 D MPM,achieving a promising accuracy of up to 100%and a loss value below 0.001.A comparison shows that the 3 D 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 3 D CNN is promising for 3 D MPM as it eliminates the interference factors.
基金supported by the Geological Society of Australia(Honours Endowment Fund)the Australian Institute of Geoscientists(Honours Bursary)by ARC LP140100267
文摘Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
基金funded by the British Geological Survey,United Kingdom(S267)the Natural Environment Research Council(NERC),United Kingdom。
文摘Novel mineral prospectivity modelling presented here applies knowledge-driven feature extraction to a datadriven machine learning approach for tungsten mineralisation.The method emphasises the importance of appropriate model evaluation and develops a new Confidence Metric to generate spatially refined and robust exploration targets.The data-driven Random ForestTM algorithm is employed to model tungsten mineralisation in SW England using a range of geological,geochemical and geophysical evidence layers which include a depth to granite evidence layer.Two models are presented,one using standardised input variables and a second that implements fuzzy set theory as part of an augmented feature extraction step.The use of fuzzy data transformations mean feature extraction can incorporate some user-knowledge about the mineralisation into the model.The typically subjective approach is guided using the Receiver Operating Characteristics(ROC)curve tool where transformed data are compared to known training samples.The modelling is conducted using 34 known true positive samples with 10 sets of randomly generated true negative samples to test the random effect on the model.The two models have similar accuracy but show different spatial distributions when identifying highly prospective targets.Areal analysis shows that the fuzzy-transformed model is a better discriminator and highlights three areas of high prospectivity that were not previously known.The Confidence Metric,derived from model variance,is employed to further evaluate the models.The new metric is useful for refining exploration targets and highlighting the most robust areas for follow-up investigation.The fuzzy-transformed model is shown to contain larger areas of high model confidence compared to the model using standardised variables.Finally,legacy mining data,from drilling reports and mine descriptions,is used to further validate the fuzzy-transformed model and gauge the depth of potential deposits.Descriptions of mineralisation corroborate that the targets generated in these models could be undercover at depths of less than 300 m.In summary,the modelling workflow presented herein provides a novel integration of knowledge-driven feature extraction with data-driven machine learning modelling,while the newly derived Confidence Metric generates reliable mineral exploration targets.
文摘Support Vector Machine(SVM) was demonstrated as a potentially useful tool to integrate multi-variables and to produce a predictive map for mineral deposits. The e 1071,a free R package,was used to construct a SVM with radial kernel function to integrate four evidence layers and to map prospectivity for Gangdese porphyry copper deposits.The results demonstrate that the predicted prospective target area for Cu occupies 20.5%of the total study area and contains 52.4%of the total number of known porphyry copper deposits.The results obtained
基金the financial support of the ARC ITTC DARE Centre IC190100031 (ML, MJ, RS, EC)the ARC DECRA scheme DE190100431 (ML)+4 种基金ARC Linkage Loop3D LP170100985 (ML, MJ, GP, JG)MRIWA Project M0557 (NP, MJ)MinEx CRC (ML, MJ, JG, GP)support from European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 101032994supported by the Mineral Exploration Cooperative Research Centre whose activities are funded by the Australian Government’s Cooperative Research Centre Program。
文摘The past two decades have seen a rapid adoption of artificial intelligence methods applied to mineral exploration. More recently, the easier acquisition of some types of data has inspired a broad literature that has examined many machine learning and modelling techniques that combine exploration criteria,or ’features’, to generate predictions for mineral prospectivity. Central to the design of prospectivity models is a ’mineral system’, a conceptual model describing the key geological elements that control the timing and location of economic mineralisation. The mineral systems model defines what constitutes a training set, which features represent geological evidence of mineralisation, how features are engineered and what modelling methods are used. Mineral systems are knowledge-driven conceptual models, thus all parameter choices are subject to human biases and opinion so alternative models are possible.However, the effect of alternative mineral systems models on prospectivity is rarely compared despite the potential to heavily influence final predictions. In this study, we focus on the effect of conceptual uncertainty on Fe ore prospectivity models in the Hamersley region, Western Australia. Four important considerations are tested.(1) Five different supergene and hypogene conceptual mineral systems models guide the inputs for five forest-based classification prospectivity models model.(2) To represent conceptual uncertainty, the predictions are then combined for prospectivity model comparison.(3)Representation of three-dimensional objects as two-dimensional features are tested to address commonly ignored thickness of geological units.(4) The training dataset is composed of known economic mineralisation sites(deposits) as ’positive’ examples, and exploration drilling data providing ’negative’sampling locations. Each of the spatial predictions are assessed using independent performance metrics common to AI-based classification methods and subjected to geological plausibility testing. We find that different conceptual mineral systems produce significantly different spatial predictions, thus conceptual uncertainty must be recognised. A benefit to recognising and modelling different conceptual models is that robust and geologically plausible predictions can be made that may guide mineral discovery.
文摘BasinMod 1D software with faulting module is used to model two synthetic wells taken from a geoseismic section in Exploration Block 2 in western Nepal to understand the burial and thermal history of exterior belt(Terai)and Siwalik fold and thrust belt.The study focuses the measured inputs of source and reservoir rocks of Surkhet Group consisting of Swat shale(2%),TOC and Melpani sandstone porosity(10%).The geohistory curves show rapid sedimentation and tectonic subsidence.The thermal history is constrained using a 20℃/km geothermal gradient for the exterior belt,whereas for the Siwalik fold and thrust belt,a two-step geothermal gradient is proposed using a 20℃/km for the upper 2,000 m and 23℃/km below this depth.The modeled values for maturity show that the Surkhet Group lies in the mid mature oil window in the exterior belt,but for the Siwalik fold and thrust belt,the hanging-wall Paleogene wedge is in the early mature stage,whereas the footwall Paleogene wedge is in the late mature stage.Oil generation for the Swat shales started at 6.3 Ma at 3,988 m depth with peak oil generation 2.4-1.3 Ma at 5,435-5,782 m depth in the exterior belt.However,the Siwalik fold and thrust belt modeling shows that the footwall Swat Formation has no oil generation capacity after the faulting episode,whereas it had been producing oil since about 8.5 Ma at 3,800 m with main phase ofoil generation at about 7 Ma at 4,600 m.The hanging-wall Swat Formation has been in the early mature stage of oil generation since faulting.The timing of structural trap formation window is set to 4.1-1.8 Ma based on geological evidence from the literature.The results show trap formation is more or less contemporaneous with hydrocarbon generation and expulsion and timing will be critical for assessments of the prospectivity.
文摘The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.
文摘Geospatial Information System (GIS) provide tools to quantitatively analysis and combination of datasets from geological, geophysical, remote sensing and geochemical surveys for decision-making processes. Excellent coverage of well-documented and good quality data enables testing of variable exploration modeling in an efficient way. The study area of this research is the most important part of Cu (Mo) porphyry—type mineralization belt in Iran. There are some well-known porphyry copper deposits in this region like Sarcheshmeh and Meiduk mines, but certainly there are same grounds to search for new porphyry deposits. The risks of developing mineral resources need to be known as accurately as possible, with regarding to all features those are effective in mineralization. These features can be recognized respect to Critical Genetic Factors (CGF’s) using Critical Recognition Criteria (CRC) for each type of mineralization. CGF’s can be employed for designing a Conceptual Genetic Model (CGM). Evidence maps create on the basis of CGM and then integrate together for production of Mineral Prospectivity Map (MPM). This map categorizes the areas based on their exploration importance. There are several techniques for creation of MPM. Interval Valued Fuzzy Sets (IVFSs) TOPSIS method was applied in this research. This method as a knowledge-driven method, allocate appropriate weights to layers on the basis of the effective membership, non membership, and non-certainty. The fundamental concept of TOPSIS is that the chosen alternatives should have the shortest distance from the positive ideal points (A*) and the farthest distance from negative ideal points (A-).
文摘Extracting and synthesizing information from existing and massive amounts of geology spatial data sets is of great scientific significance and has considerable value in its applications. To make mineral exploration less expensive, more efficient, and more accurate, it is important to move beyond traditional concepts and establish a rapid, efficient, and intelligent method of predicting the existence and location of minerals. This paper describes a case-based reasoning (CBR) method for mineral prospectivity mapping that takes spatial features of geology data into account and offers an intelligent approach. This method include a metallogenic case representation that combines spatial and attribute features, metallogenic case-based storage organization, and a metallogenic case similarity retrieval model. The experiments were performed in the eastern Kunlun Mountains, China using CBR and weights-of-evidence (WOE), respectively. The results show that the prediction accuracy of the CBR is higher than that of the WOE.
文摘In this study,we present a machine learning-based method to predict trace element concentrations from major and minor element concentration data using a legacy lithogeochemical database of magmatic rocks from the Karoo large igneous province(Gondwana Supercontinent).Wedemonstrate that a variety of trace elements,including most of the lanthanides,chalcophile,lithophile,and siderophile elements,can be predicted with excellent accuracy.This finding reveals that there are reliable,high-dimensional elemental associations that can be used to predict trace elements in a range of plutonic and volcanic rocks.Since the major and minor elements are used as predictors,prediction performance can be used as a direct proxy for geochemical anomalies.As such,our proposed method is suitable for prospective exploration by identifying anomalous trace element concentrations.Compared to multivariate compositional data analysis methods,the new method does not rely on assumptions of stoichiometric combinations of elements in the data to discover geochemical anomalies.Because we do not use multivariate compositional data analysis techniques(e.g.principal component analysis and combined use of major,minor and trace elements data),we also show that log-ratio transforms do not increase the performance of the proposed approach and are unnecessary for algorithms that are not spatially aware in the feature space.Therefore,we demonstrate that high-dimensional elemental associations can be modelled in an automated manner through a data-driven approach and without assumptions of stoichiometry within the data.The approach proposed in this study can be used as a replacement method to the multivariate compositional data analysis technique that is used for prospectivity mapping,or be used as a pre-processor to reduce the detection of false geochemical anomalies,particularly where the data is of variable quality.
文摘Application of regional geophysical methods for hydrogeological purposes has increased over the last two decades especially in arid and semi-arid areas. A project to map the Kraaipan granite-greenstone terrain in southeast Botswana has recently been undertaken using regional aeromagnetic and gravity data with the aim to map the rocks at depth to understand the geology while the secondary objective was to subsequently assess the mineralization and groundwater potential in the area. An integrated analysis of the aeromagnetic and gravity data and their derived/processed products is hereby investigated for groundwater for drinking and agricultural purposes. The studies include: subsurface characterisation and delineation of structural framework suitable for groundwater exploration and determination of petrophysical relationships used to link the geophysical properties (e.g., density) to hydrological properties (e.g., porosity). The results of interpretation indicate that the rocks are under ~50 m of Kalahari cover and the study area is composed of three aquifers: the extensive hard rock aquifer (granitic and volcanic), the important (fractured) karst aquifer and the minor sedimentary aquifer. The area is dissected by an ENE-to-EW-trending dyke swarm visible on the regional aeromagnetic data and much clearer on high resolution aeromagnetic data. Minor fault and/or dyke elements of NW-SE and NE-SW trend are observed. Spectral analysis reveals three main average ensample interfaces at depths of 0.7 km, 1.99 km and 4.8 km. The linear Euler solutions maps reveal that the majority depths to top of magnetic bodies range from 40 m to 400 m throughout the survey area. The shallowest depths are the most significant one in this case as they probably relate to depth of bedrock and thickness of regolith or thickest sediments. For 2695 existing boreholes analysed, maximum borehole depth is 482 m (mean 108 m), and almost half (1263) were dry with another 972 having low yield (1 - 5 m3/hr) and 432 yielding 6 - 49 m3/hr and only 28 above 50 m3/hr (maximum ~160 m3/hr) and an average water strike of 64 m. There is very little correlation between interpreted hydrogeological features and the existing borehole locations. The study shows the importance of preliminary geophysical investigations before ground borehole siting and drilling in order to improve borehole success rates and/or reduce costs inherent in groundwater projects.
文摘The reason of this research is to identify the favorable areas for copper, zinc, and lead mineralization in the western part of the 1:100,000 Tafresh geological Sheet in the Urmia-Dokhtar structural zone of Iran. Effective data layers for mineralization, such as geology, geochemistry, structures, and satellite images, were analyzed and then integrated using the AHP-OWA method to identify favorable areas. Geochemical stream samples were analyzed by univariate, multivariate, and classical statistical methods and revealed the first, second, and third class anomalies for copper, zinc, and lead in the study region. Detection of hydrothermal alteration zones by Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite imagery in various algorithms, such as Relative absorption Band Depth (RBD), Minimum Noise Fraction (MNF), and Least Square Fit (LS-Fit), shows that argillic, phyllic, propylitic, and iron oxide alterations develop around the faults in the area under study. The favorable areas for copper, zinc, and lead mineralization have been identified by a combination of evidence maps of lithology, faults, dikes, geochemistry, and alteration data layers. Field observations in the area under study have confirmed the results.
文摘The Kalatereshm is an area in north of Iran which covers some part of Torud magmatic belt. The area of this belt is about 2000 square kilometers and most of the mines in this particular area are of Copper, lead and Zinc. The Synthesis process is done by the Analysis Hierarchy Process (AHP) and Index Overlay (IO) methods. Of previous studies on the area, various companies providing Geological maps and in particular the company of Jiangxi providing its own geochemical maps can be mentioned. The reasons for doing this research and its innovation in Kalatereshm’s sheet can be justified as to be valuable and the fact that we would be able to save in time and cost by doing so. Previous case studies on this particular region lacked the necessary use of an advanced software and method. The informational layers included geochemical layers (the second and first ratings were given to Copper and Lead respectively by weighting based on AHP method), geology layer (the fourth and second ratings were given to Copper and Lead respectively by weighing based on AHP method), fault layer (the first and fourth ratings were given to Copper and Lead respectively by weighting based on AHP method), satellite imagery layer (the third rating was given to both Copper and Lead by weighting based on AHP method) and the more applicable areas for field exploration and detailed procedures of exploration had been determined (the mentioned ratings were delineated by each element’s respective weight in each layer and their importance in the Synthesis of informational layers).
文摘This paper evaluates the hydrocarbon prospectivity and play risks of “Bob” field in Niger Delta Basin, Nigeria. The aim is to enhance exploration success through improved approach/technique by incorporating risk analysis that previous studies have not fully considered. This approach combines a set of analyses including stratigraphic/structural, amplitude, petrophysical parameter, volumetric and play risk using a suite of well logs and 3D seismic data. Maximum amplitude anomaly map extracted on the surfaces of delineated 3 reservoirs revealed 6 prospects, namely: Dippers, Cranes, Turacos, Nicators, Jacanas and Pelicans with hydrocarbon accumulation. Petrophysical analysis showed ranges of values for porosity, permeability and water saturation of 0.21 to 0.23, 158.96 to 882.39 mD, and 0.07 to 0.11, respectively. The various prospects yielded the following stock tank volumes 12.73, 6.84, 3.84, 11.32, 7.42 and 4.76 Million barrels (Mbls) each respectively in a column of 66 ft reservoir sand in the study area. Play risk analysis results gave: Pelicans and Nicators (low), Turacos and Dippers (moderate), while Jacanas and Cranes show high risk with minimal promise for good oil accumulation. The prospects possess good reservoir petrophysical properties with low to moderate risk, thus, viable for commercial hydrocarbon production, which increases confidence in management decisions for production.
文摘Mineral exploration campaigns are financially risky.Several state-of-the-art methods have been developed to mitigate the risk,including predictive modelling of mineral prospectivity using principal component analysis(PCA)and geographic information systems(GIS).The PCA and GIS approach is currently considered acceptable for generating mineral exploration targets.However,some of its limitations are the dependence on sample stoichiometry(e.g.,the existence of minerals),the necessity of log-ratio transformations when dealing with compositional data,and manual interpretation and use of principal components to enhance potential geochemical anomalies for prospectivity mapping.In this study,we generalize the fundamental ideas behind the PCA and GIS approach by developing a new data-driven approach using ML.We showcase a new workflow capable of generating either intermediate evidence layers or final prospectivity maps that depict major regional geochemical anomalies using multi-element geochemical data from Southeastern Churchill Province(Quebec and Labrador),Canada.The region is known for its REEs endowment and the data were gathered for prospectivity mapping.A comparison with the established multivariate hybrid data-and knowledge-based approach revealed that on a roughly comparable basis of the amount of manual effort,our new data-driven procedure can much more accurately identify geochemical anomalies in both univariate and multivariate applications.The results of our prospectivity mapping corroborate with the ground truth or known geological anomalies in the studied region.These findings have potentially wider implications on exploration target generation,where project risks(financial,environmental,political,etc.)and geochemical anomalies must be quantified using robust and effective datadriven approaches.In addition,our methodology is more replicable and objective,as manual geoscientific interpretation is not required during the detection of geochemical anomalies.
文摘Bangladesh is a country with limited sources of energy and since 1990 natural gas has been its main source of energy. Most of the exploration approaches had been conducted onshore especially in the central and eastern part of Bangladesh, particularly in northeastern Sylhet basin. Among the hydrocarbon provinces, the East Delta Hill Tract province is an under explored petroleum province in Bangladesh. An exploratory well drilled in Sitakund anticline was found dry but no reasonable cause was perceived why that well went dry. Although many works had been carried out in Chittagong Hill Tracts but none of them was cumulative and descriptive. In this study, the overall hydrocarbon prospect of the Chittagong Hill Tracts was analyzed by mapping of potential zones on the basis of the evaluation of regional structure and construction of lithocolumn of the prospective zones. The five elements of the petroleum system discussed thoroughly to find overall petroleum prospect of the study area. Source rocks of Chittagong Hill Tracts are mainly Bhuban shale, reservoir rock is sandstone from Bhuban-Bokabil formation, the way of migration path is both through longitudinal and cross fault. The data of source rock and seal is collected from previous researches. Multiple types of traps have been found there. Conventional anticlinal traps which are highly disturbed due to tectonic instability & the core part are shale diapirism. Most of the anticlines are plunging and the nose or plunge area might be prospective to HC for being comparatively less faulted. Broad synclinal areas between tight narrow anticlines are another prospective area for HC. These similar types of synclines are also found in Tripura, India which is a highly prospective area for petroleum and their small anticlinal hums within syncline are also prospective. Some stratigraphic traps have also been found in Tripura from where production has been started already. Both Chittagong Hill Tracts and Tripura Fold Belts are parts of great Arakan Fold Belts, so similar type of structures might be prospective here. Apart from these, Bangladesh is a deltaic country. So stratigraphic trapment like channel sand, pinch-outs is possible. Considering all the elements of petroleum prospectivity of the area and factors discussed above, it is quite clear that Chittagong hill tracts might be the next target for HC exploration program.
