Geochemical Anomalies Identified by Multifractal Modeling: Implications for Mineral Exploration in the Ziyoutun Cu-Au District, Jilin Province, China

The Ziyoutun Cu-Au district is located in the Jizhong–Yanbian Metallogenic Belt and possesses excellent prospects. However, the thick regolith and complex tectonic settings present challenges in terms of detecting and decomposition of weak geochemical anomalies. To address this challenge, we initially conducted a comprehensive analysis of 1:10,000-scale soil geochemical data. This analysis included multivariate statistical techniques, such as correlation analysis, R-mode cluster analysis, Q–Q plots and factor analysis. Subsequently, we decomposed the geochemical anomalies, identifying weak anomalies using spectrum-area modeling and local singularity analysis. The results indicate that the assemblage of Au-Cu-Bi-As-Sb represents the mineralization at Ziyoutun. In comparison to conventional methods, spectrumarea modeling and local singularity analysis outperform in terms of identification of anomalies. Ultimately, we considered four specific target areas(AP01, AP02, AP03 and AP04) for future exploration, based on geochemical anomalies and favorable geological factors. Within AP01 and AP02, the geochemical anomalies suggest potential mineralization at depth, whereas in AP03 and AP04 the surface anomalies require additional geological investigation. Consequently, we recommend conducting drilling, following more extensive surface fieldwork, at the first two targets and verifying surface anomalies in the last two targets. We anticipate these findings will significantly enhance future exploration in Ziyoutun.

Age and Genesis of the Laodaizhanggou Pb-Zn-Ag Deposit in the Fudian Ore Field,Southern North China Craton:Implications for Regional Mineral Prospecting

The Fudian ore field in the southern North China Craton hosts the giant Donggou porphyry Mo deposit and several Pb-Zn-Ag vein deposits. Ore genesis of the Pb-Zn-Ag deposits and their relationships with the Donggou porphyry-related system are still controversial, which further restricts the regional prospecting and exploration. The Laodaizhanggou Pb-Zn-Ag deposit in the northwest of the ore field was focused in this study, to investigate its ore-forming age and genesis, and further to explore the implications for regional prospecting of Pb-Zn-Ag and Mo. The Pb-Zn-Ag veins at Laodaizhanggou are structurally controlled by the east-striking fault zones transecting the host volcanic rocks of Proterozoic Xiong’er Group. Field observations and textural relationships indicate that there are four paragenetic stages during ore-forming process, including the quartz-pyrite veins(stage I), siderite-polymetallic sulfide veins(stage II), ankerite-polymetallic sulfide veins(stage III), and quartz-calcite veins(stage IV). Ore-related sericite 40 Ar/39 Ar dating shows that the Pb-Zn-Ag mineralization at Laodaizhanggou was formed at 124.7±1.2 Ma. Carbonate minerals(siderite, ankerite, and calcite) have δ13 CPDB values of-9.1‰ to-3.9‰ and δ18 OSMOW of 12.1‰ to 15.6‰, corresponding to calculated values for the ore fluids of-8.0‰ to-2.8‰ and 4.9‰ to 10.1‰, respectively. These isotope values are in accordance with those of magmatic fluids. Sulfide minerals at Laodaizhanggou have δ34 S values of 5.3‰ to 10.1‰, and galena separates have 206 Pb/204 Pb ratios of 17.380 to 17.458, 207 Pb/204 Pb ratios of 15.459 to 15.485, and 208 Pb/204 Pb ratios of 38.274 to 38.370. Both S and Pb isotope data of Laodaizhanggou are consistent with those of the Donggou porphyry Mo deposit and distal Sanyuangou and Wangpingxigou Pb-Zn-Ag deposits, suggesting they share a similar magmatic origin. However, the Laodaizhanggou deposit was not the distal product of the giant Donggou porphyry-related magmatic-hydrothermal system, as the former is about 7 Ma older than the latter. The ore-forming age of Laodaizhanggou is consistent with that of the phase 1 magmatism of Taishanmiao batholith, indicating the Laodaizhanggou deposit is genetically related to ca. 125 Ma magmatism in the area. Combined the geochronological and geochemical data on Laodaizhanggou and the regional geological setting, we propose that the fracture systems in the northeast of the Taishanmiao batholith are potential sites for prospecting Pb-Zn-Ag deposit and the deep part among Laodaizhanggou, Xizaogou, and Liezishan is a target for prospecting porphyry Mo deposit.

Soil geochemical prospecting prediction method based on deep convolutional neural networks-Taking Daqiao Gold Deposit in Gansu Province, China as an example

A method is proposed for the prospecting prediction of subsurface mineral deposits based on soil geochemistry data and a deep convolutional neural network model.This method uses three techniques(window offset,scaling,and rotation)to enhance the number of training data for the model.A window area is used to extract the spatial distribution characteristics of soil geochemistry and measure their correspondence with the occurrence of known subsurface deposits.Prospecting prediction is achieved by matching the characteristics of the window area of an unknown area with the relationships established in the known area.This method can efficiently predict mineral prospective areas where there are few ore deposits used for generating the training dataset,meaning that the deep-learning method can be effectively used for deposit prospecting prediction.Using soil active geochemical measurement data,this method was applied in the Daqiao area,Gansu Province,for which seven favorable gold prospecting target areas were predicted.The Daqiao orogenic gold deposit of latest Jurassic and Early Jurassic age in the southern domain has more than 105 t of gold resources at an average grade of 3-4 g/t.In 2020,the project team drilled and verified the K prediction area,and found 66 m gold mineralized bodies.The new method should be applicable to prospecting prediction using conventional geochemical data in other areas.

Structural Control of Ore Mineralization in the Southeastern Margin of Western Nigeria Basement

Structural analyses are often ignored in mineral prospecting endeavor of any geological terrain despite the importance of geological structures in the formation of ore deposits. This paper correlates the results of mineral prospecting campaign to those of the structural analysis in the southeastern margin of the western Nigeria basement. The mineral prospecting was executed by essentially geochemical-exploration methods, while the structural analysis was achieved by lineament analysis using Landsat-8 imagery. The mineral prospecting campaign eventually led to the discovery of gold bearing marble deposit in the Dagbala area;silver and copper bearing silicified sheared rock in the Dagbala, Ojirami, Erurhu and Atte areas;lead and zinc bearing metaconglomerate around Egbigele;uranium and thorium bearing pegmatite in the Dagbala area. The Landsat-8 lineament analysis showed the presence of a shear zone in the central, folds in the SW, and fractures in the NE parts of the study area. The relation between the two is such that the gold and silver-copper mineralization is associated with the shear zone, the lead-zinc mineralization to the folds, and the uranium-thorium mineralization to the fractures. Indeed, geological structures guide mineralization and their analysis can be employed for mineral prospecting.

Explainable artificial intelligence models for mineral prospectivity mapping

Mineral prospectivity mapping(MPM)is designed to reduce the exploration search space by combining and analyzing geological prospecting big data.Such geological big data are too large and complex for humans to effectively handle and interpret.Artificial intelligence(AI)algorithms,which are powerful tools for mining nonlinear mineralization patterns in big data obtained from mineral exploration,have demonstrated excellent performance in MPM.However,AI-driven MPM faces several challenges,including difficult interpretability,poor generalizability,and physical inconsistencies.In this study,based on previous studies,we devised a novel workflow that aims to constructing more transparent and explainable artificial intelligence(XAI)models for MPM by embedding domain knowledge throughout the AI-driven MPM,from input data to model design and model output.This newly proposed approach provides strong geological and conceptual leads that guide the entire AI-driven MPM model training process,thereby improving model interpretability and performance.Overall,the development of XAI models for MPM is capable of embedding prior and expert knowledge throughout the modeling process,presenting a valuable and promising area for future research designed to improve MPM.

A machine learning approach to tungsten prospectivity modelling using knowledge-driven feature extraction and model confidenc

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.

Space-associated domain adaptation for three-dimensional mineral prospectivity modeling

Geographical information systems(GIS)are essential tools for mineral prospectivity modeling(MPM).Three-dimensional(3D)MPM is able to learn the association between geological evidence and mineralization in shallow zones and thereby build a prospectivity model for deep zones,making it a desirable technique to target deep-seated orebodies.However,existing 3D MPM methods directly generalize the model learned in shallow zones to the deep zones without attention to model transferability caused by the different metallogenic mechanisms between the two zones.In this study,we aim to robustly transfer the prospectivity model learned from shallow zones to deep zones.We cast the 3D MPM as a domain adaptation problem,which is an important realm of transfer learning.Because the metallogenic mechanism can be closely associated with spatial locations,we specifically focus on domain adaption concerning the spatial locations that are ignored by conventional domain adaptation methods.To measure the spatial-associated domain discrepancy,we propose a novel spatial-associated maximum mean discrepancy(SAMMD),which compares the joint distributions of features and spatial locations across domains.Based on the SAMMD criterion,a deep neural network,referred to as the spatial-associated domain adaptation network,is devised to learn cross-domain but mineralization-indicative features for building prospectivity model that is transferable to deep zones.A case study of the world-class Sanshandao gold deposit,in eastern China,was carried out to validate the effectiveness of the proposed methods.The results show that compared with other leading MPM methods and other domain adaption variants,the proposed method has superior prediction accuracy and targeting efficiency,demonstrating the effectiveness and robustness of the proposed method in targeting deep-seated orebodies in areas with different metallogenic mechanisms and no labeled data.

Nonferrous Geological Prospecting Institutions Discover Important Mineral and Rare Metal Reserves

The quadrennial Secretary General’s meeting of the Geological and Mineral Resources Branch of the China Nonferrous Metals Industry Association(CNMIA)took place on April 9.It’s learned from the meeting that nonferrous geological prospecting institutions across the country completed 3,901 geological

Mineral Prospectivity Prediction via Convolutional Neural Networks Based on Geological Big Data

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.

A comparative study of fuzzy weights of evidence and random forests for mapping mineral prospectivity for skarn-type Fe deposits in the southwestern Fujian metallogenic belt, China

Recent studies have pointed out that the widespread iron deposits in southwestern Fujian metallogenic belt(SFMB)(China) are skarn-type deposits associated with the Yanshanian granites. There is still excellent potential for mineral exploration because large areas in this belt are covered by forest. A new predictive model for mapping skarn-type Fe deposit prospectivity in this belt was developed and focused on in this study, using five criteria as evidence:(1) the contact zones of Yanshanian granites(GRANITE);(2) the contact zones within the late Paleozoic marine sedimentary rocks and the carbonate formations(FORMATION);(3) the NE-NNE-trending faults(FAULT);(4) the zones of skarn alterations(SKARN); and(5) the aeromagnetic anomaly(AEROMAGNETIC). The fuzzy weights of evidence(FWof E) method, developed from the classical weights of evidence(Wof E) and based on fuzzy sets and fuzzy probabilities, could provide smaller variances and more accurate posterior probabilities and could effectively minimize the uncertainty caused by omitted or wrongly assigned data and be more flexible than the Wof E. It is an efficient and widely used method for mineral potential mapping. Random forests(RF) is a new and useful method for data-driven predictive mapping of mineral prospectivity method, and needs further scrutiny. Both prospectivity results respectively using the FWof E and RF methods reveal that the prediction model for the skarn-type Fe deposits in the SFMB is successful and efficient. Both methods suggested that the GRANITE and FORMATION are the most valuable evidence maps, followed by SKARN, AEROMAGNETIC, and FAULT. This is coincident with the skarn-type Fe deposit mineral model in the SFMB. The unstable performance experienced when FORMATION was omitted might indicate that the highest uncertainty and risk in follow-up exploration is related to the sequences. In addition, the performance of the RF method for the skarn-type Fe deposits prospectivity in the SFMB is better than the FWof E; therefore, it could be used to guide further exploration of skarn-type Fe prospects in the SFMB.

Towards a fully data-driven prospectivity mapping methodology:A case study of the Southeastern Churchill Province,Québec and Labrador

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(Qu
ebec 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.