Distinguishing geochemical anomalies from background is a basic task in exploratory geochemistry. The derivation of geochemical anomalies from stream sediment geochemical data and the decomposition of these anomalies ...Distinguishing geochemical anomalies from background is a basic task in exploratory geochemistry. The derivation of geochemical anomalies from stream sediment geochemical data and the decomposition of these anomalies into their component patterns were described. A set of stream sediment geochemical data was obtained for 1 880 km 2 of the Pangxidong area, which is in the southern part of the recently recognized Qinzhou-Hangzhou joint tectonic belt. This belt crosses southern China and tends to the northwest (NE) direction. The total number of collected samples was 7 236, and the concentrations of Ag, Au, Cu, As, Pb and Zn were measured for each sample. The spatial combination distribution law of geochemical elements and principal component analysis (PCA) were used to construct combination models for the identification of combinations of geochemical anomalies. Spectrum-area (S-A) fractal modeling was used to strengthen weak anomalies and separate them from the background. Composite anomaly modeling was combined with fractal filtering techniques to process and analyze the geochemical data. The raster maps of Au, Ag, Cu, As, Pb and Zn were obtained by the multifractal inverse distance weighted (MIDW) method. PCA was used to combine the Au, Ag, Cu, As, Pb, and Zn concentration values. The S-A fractal method was used to decompose the first component pattern achieved by the PCA. The results show that combination anomalies from a combination of variables coincide with the known mineralization of the study area. Although the combination anomalies cannot reflect local anomalies closely enough, high-anomaly areas indicate good sites for further exploration for unknown deposits. On this basis, anomaly and background separation from combination anomalies using fractal filtering techniques can provide guidance for later work.展开更多
Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to pr...Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.展开更多
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 an...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.展开更多
A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that a...A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that are constructed on each processed datum, can be used to separate various types of geochemical and geophysical anomalies. The basic model, with an emphasis on the GIS based implementation and the application to the geochemical and geophysical data processing for mineral exploration in southern Nova Scotia, Canada, indicates its advantage in the separation of multiple anomalies from the background.展开更多
89 Au geochemical anomalies are delineated by using 1/200000 regional geochemical exploration data. By researching regional geochemical characteristics and the relationship with the geological background, the author p...89 Au geochemical anomalies are delineated by using 1/200000 regional geochemical exploration data. By researching regional geochemical characteristics and the relationship with the geological background, the author points out that： the main factors causing high background of Au geochemical anomalies are Gaixian and Dashiqiao formation of Liaohe group, intrusions of Mesozoic intermediate-acid intrusive rocks. The elements combination types of typical anomalies are determined by using factorial analysis,cluster analysis and other mathematical methods with the combination of elements association in typical anomalies：the composite anomaly of Baiyun gold deposits is Au-As-Sb, Maoling gold deposit is Au-As- Bi-Mo, Wulong gold deposits is Au-As-Bi-W, Xiaotongjiapuzi gold deposit is Au-As-Bi-Mo-Sb. By using multivariate statistical analysis method,62 ore-caused anomaly are preferred in 89 Au geochemical anomalies delineated. On this basis, the 62 anomalies are divided into 4 kinds of anomaly types reference to elements combination types of typical anomalies,the classification results of ore-caused anomalies are： 4 geochemical anomalies of Baiyun type,36 geochemical anomalies of Maoling type,11 geochemical anomalies of Wulong type, 11 geochemical anomalies of Xiaotongjapuzi type. According to the results, the prospecting direction is provided for the futme of gold exploration.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
Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwes...Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms （the hierarchical, k-means, and fuzzy cmeans algorithms） and six data transformation methods （the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT） are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of columnor variable-based （R-type） cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based （Q-type） cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy cmeans clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.展开更多
Isolation forest and elliptic envelope are used to detect geochemical anomalies,and the bat algorithm was adopted to optimize the parameters of the two models.The two bat-optimized models and their default-parameter c...Isolation forest and elliptic envelope are used to detect geochemical anomalies,and the bat algorithm was adopted to optimize the parameters of the two models.The two bat-optimized models and their default-parameter counterparts were used to detect multivariate geochemical anomalies from the stream sediment survey data of 1:50000 scale collected from the Helong district,Jilin Province,China.Based on the data modeling results,the receiver operating characteristic(ROC)curve analysis was performed to evaluate the performance of the two bat-optimized models and their default-parameter counterparts.The results show that the bat algorithm can improve the performance of the two models by optimizing their parameters in geochemical anomaly detection.The optimal threshold determined by the Youden index was used to identify geochemical anomalies from the geochemical data points.Compared with the anomalies detected by the elliptic envelope models,the anomalies detected by the isolation forest models have higher spatial relationship with the mineral occurrences discovered in the study area.According to the results of this study and previous work,it can be inferred that the background population of the study area is complex,which is not suitable for the establishment of elliptic envelope model.展开更多
基金Project(1212010071012) supported by Guangdong Pangxidong Mineral Prospect Investigation, ChinaProject(41004051) supported by the National Natural Science Foundation of ChinaProject ([2007]038-01-18) supported by Nationwide Mineral Resource Potential Evaluation Projects of Ministry of Land and Resources, China
文摘Distinguishing geochemical anomalies from background is a basic task in exploratory geochemistry. The derivation of geochemical anomalies from stream sediment geochemical data and the decomposition of these anomalies into their component patterns were described. A set of stream sediment geochemical data was obtained for 1 880 km 2 of the Pangxidong area, which is in the southern part of the recently recognized Qinzhou-Hangzhou joint tectonic belt. This belt crosses southern China and tends to the northwest (NE) direction. The total number of collected samples was 7 236, and the concentrations of Ag, Au, Cu, As, Pb and Zn were measured for each sample. The spatial combination distribution law of geochemical elements and principal component analysis (PCA) were used to construct combination models for the identification of combinations of geochemical anomalies. Spectrum-area (S-A) fractal modeling was used to strengthen weak anomalies and separate them from the background. Composite anomaly modeling was combined with fractal filtering techniques to process and analyze the geochemical data. The raster maps of Au, Ag, Cu, As, Pb and Zn were obtained by the multifractal inverse distance weighted (MIDW) method. PCA was used to combine the Au, Ag, Cu, As, Pb, and Zn concentration values. The S-A fractal method was used to decompose the first component pattern achieved by the PCA. The results show that combination anomalies from a combination of variables coincide with the known mineralization of the study area. Although the combination anomalies cannot reflect local anomalies closely enough, high-anomaly areas indicate good sites for further exploration for unknown deposits. On this basis, anomaly and background separation from combination anomalies using fractal filtering techniques can provide guidance for later work.
文摘Interference in the data of geochemical hydrocarbon exploration is a large obstacle for anomaly recognition. The multiresolution analysis of wavelet analysis can extract the information at different scales so as to provide a powerful tool for information analysis and processing. Based on the analysis of the geometric nature of hydrocarbon anomalies and background, Mallat wavelet and symmetric border treatment are selected and data pre-processing (logarithm-normalization) is established. This approach provide good results in Shandong and Inner Mongolia, China. It is demonstrated that this approach overcome the disadvantage of backgound variation in the window (interference in window), used in moving average, frame filtering and spatial and scaling modeling methods.
基金project was supported by the Enterprise Authorized Item from the Jilin Sanhe Mining Development Co., Ltd. (3-4-2021-120)the Fundamental Research Funds for the Central Universities (2-9-2020-010)。
文摘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.
文摘A recently developed method, on the bases of “multifractal spectrum” filters for mineral exploration, is introduced in this paper. The “multifractal spectrum” filters, a group of irregularly shaped filters that are constructed on each processed datum, can be used to separate various types of geochemical and geophysical anomalies. The basic model, with an emphasis on the GIS based implementation and the application to the geochemical and geophysical data processing for mineral exploration in southern Nova Scotia, Canada, indicates its advantage in the separation of multiple anomalies from the background.
文摘89 Au geochemical anomalies are delineated by using 1/200000 regional geochemical exploration data. By researching regional geochemical characteristics and the relationship with the geological background, the author points out that： the main factors causing high background of Au geochemical anomalies are Gaixian and Dashiqiao formation of Liaohe group, intrusions of Mesozoic intermediate-acid intrusive rocks. The elements combination types of typical anomalies are determined by using factorial analysis,cluster analysis and other mathematical methods with the combination of elements association in typical anomalies：the composite anomaly of Baiyun gold deposits is Au-As-Sb, Maoling gold deposit is Au-As- Bi-Mo, Wulong gold deposits is Au-As-Bi-W, Xiaotongjiapuzi gold deposit is Au-As-Bi-Mo-Sb. By using multivariate statistical analysis method,62 ore-caused anomaly are preferred in 89 Au geochemical anomalies delineated. On this basis, the 62 anomalies are divided into 4 kinds of anomaly types reference to elements combination types of typical anomalies,the classification results of ore-caused anomalies are： 4 geochemical anomalies of Baiyun type,36 geochemical anomalies of Maoling type,11 geochemical anomalies of Wulong type, 11 geochemical anomalies of Xiaotongjapuzi type. According to the results, the prospecting direction is provided for the futme of gold exploration.
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.
基金The authors thank Ratheesh Kumar R.T, Rustam Orozbaev for their assistance to revise the language before we submit the manuscript and the authors are grateful for the anonymous reviewers＇ constructive comments and suggestions. This study was funded by the National Natural Science Foundation of China （Grant Nos. U1503291 and 41402296）, and a Major Project in Xinjiang Uygur Autonomous Region （201330121-3）.
文摘Cluster analysis is a well-known technique that is used to analyze various types of data. In this study, cluster analysis is applied to geochemical data that describe 1444 stream sediment samples collected in northwestern Xinjiang with a sample spacing of approximately 2 km. Three algorithms （the hierarchical, k-means, and fuzzy cmeans algorithms） and six data transformation methods （the z-score standardization, ZST; the logarithmic transformation, LT; the additive log-ratio transformation, ALT; the centered log-ratio transformation, CLT; the isometric log-ratio transformation, ILT; and no transformation, NT） are compared in terms of their effects on the cluster analysis of the geochemical compositional data. The study shows that, on the one hand, the ZST does not affect the results of columnor variable-based （R-type） cluster analysis, whereas the other methods, including the LT, the ALT, and the CLT, have substantial effects on the results. On the other hand, the results of the row- or observation-based （Q-type） cluster analysis obtained from the geochemical data after applying NT and the ZST are relatively poor. However, we derive some improved results from the geochemical data after applying the CLT, the ILT, the LT, and the ALT. Moreover, the k-means and fuzzy cmeans clustering algorithms are more reliable than the hierarchical algorithm when they are used to cluster the geochemical data. We apply cluster analysis to the geochemical data to explore for Au deposits within the study area, and we obtain a good correlation between the results retrieved by combining the CLT or the ILT with the k-means or fuzzy c-means algorithms and the potential zones of Au mineralization. Therefore, we suggest that the combination of the CLT or the ILT with the k-means or fuzzy c-means algorithms is an effective tool to identify potential zones of mineralization from geochemical data.
基金supported by the National Natural Science Foundation of China(Nos.41672322,41872244)。
文摘Isolation forest and elliptic envelope are used to detect geochemical anomalies,and the bat algorithm was adopted to optimize the parameters of the two models.The two bat-optimized models and their default-parameter counterparts were used to detect multivariate geochemical anomalies from the stream sediment survey data of 1:50000 scale collected from the Helong district,Jilin Province,China.Based on the data modeling results,the receiver operating characteristic(ROC)curve analysis was performed to evaluate the performance of the two bat-optimized models and their default-parameter counterparts.The results show that the bat algorithm can improve the performance of the two models by optimizing their parameters in geochemical anomaly detection.The optimal threshold determined by the Youden index was used to identify geochemical anomalies from the geochemical data points.Compared with the anomalies detected by the elliptic envelope models,the anomalies detected by the isolation forest models have higher spatial relationship with the mineral occurrences discovered in the study area.According to the results of this study and previous work,it can be inferred that the background population of the study area is complex,which is not suitable for the establishment of elliptic envelope model.
