Experiences with 30 years of geochemical mapping at the (sub)continental scale in Europe using a wide range of different sample media are reviewed and discussed with a focus on the most recent GEMAS (GEochemical Mapping of Agricultural Soils) project. Comparing results from the different surveys it is possible to come to conclusions how geochemical surveys at the continental scale could best be designed. High analytical quality and as low detection limits as possible are key requirements. In Europe good experiences were achieved with the <2 mm fraction of soil samples and aqua regia extraction. Focus should rather be on high quality of sampling and analyses and more determined parameters than on more samples. The sample density of 1 site/2500 km 2 provides a good overview of the processes governing geochemistry at the continental scale. Results should be extensively published by the project team to get the dataset known and utilized by the wider scientific community. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
{"title":"Experiences from 30 years of low density geochemical mapping at the subcontinental to continental scale in Europe","authors":"C. Reimann","doi":"10.1144/geochem2022-030","DOIUrl":"https://doi.org/10.1144/geochem2022-030","url":null,"abstract":"\u0000 Experiences with 30 years of geochemical mapping at the (sub)continental scale in Europe using a wide range of different sample media are reviewed and discussed with a focus on the most recent GEMAS (GEochemical Mapping of Agricultural Soils) project. Comparing results from the different surveys it is possible to come to conclusions how geochemical surveys at the continental scale could best be designed. High analytical quality and as low detection limits as possible are key requirements. In Europe good experiences were achieved with the <2 mm fraction of soil samples and aqua regia extraction. Focus should rather be on high quality of sampling and analyses and more determined parameters than on more samples. The sample density of 1 site/2500 km\u0000 2\u0000 provides a good overview of the processes governing geochemistry at the continental scale. Results should be extensively published by the project team to get the dataset known and utilized by the wider scientific community.\u0000 \u0000 \u0000 Thematic collection:\u0000 This article is part of the Continental-scale geochemical mapping collection available at:\u0000 https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping\u0000","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44045806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Understanding the process and influencing factors of the oxidation of pyrite is beneficial for the management of environmental problems in mining areas. In this study, we investigated the morphology and geochemistry of the pyrite and related goethite from the weathering crust of the Shangmanggang (SMG) gold deposit, southwest China, via petrographic work, electron microprobe analysis, X-ray diffraction analysis, and PHREEQC geochemical modelling. The weathering profile of the SMG is composed of the unweathered Carlin-type zone, the semi-weathered zone, and the highly weathered red-clay zone, and different types of pyrite, framboidal pyrite (Py1), cube pyrite (Py2), and zoned pyrite (Py3), were differentially oxidized and transferred into corresponding pyrite-pseudomorphic goethite commonly comprised of the early and late phase. Furthermore, the stronger oxidation is related to more late goethite with more Al and Si content. The ubiquitous dolomite buffer kept the pH of the pore fluid neutral, resulting in the precipitation and accumulation of a goethite coating around pyrite, which further reduced the oxidation rate and formed pyrite-pseudomorphic goethite ultimately via coupled dissolution-reprecipitation reactions. In addition, the different mineralogical properties resulted in the differential oxidation of pyrite such that the smaller grains oxidized faster, and As within the pyrite accelerated the oxidation. Moreover, the rate-limited oxidation of pyrite under carbonate buffer prevents acid mine drainage (AMD) from forming and limits As release from arsenian pyrite into the external environment. Supplementary material: https://doi.org/10.6084/m9.figshare.c.6267700
{"title":"Pyrite oxidation under carbonate buffer and its environmental implications: a case study from the Shangmanggang gold deposit, southwest China","authors":"Yanyan Wang, Xuemin Liu, Qi Li","doi":"10.1144/geochem2022-021","DOIUrl":"https://doi.org/10.1144/geochem2022-021","url":null,"abstract":"Understanding the process and influencing factors of the oxidation of pyrite is beneficial for the management of environmental problems in mining areas. In this study, we investigated the morphology and geochemistry of the pyrite and related goethite from the weathering crust of the Shangmanggang (SMG) gold deposit, southwest China, via petrographic work, electron microprobe analysis, X-ray diffraction analysis, and PHREEQC geochemical modelling. The weathering profile of the SMG is composed of the unweathered Carlin-type zone, the semi-weathered zone, and the highly weathered red-clay zone, and different types of pyrite, framboidal pyrite (Py1), cube pyrite (Py2), and zoned pyrite (Py3), were differentially oxidized and transferred into corresponding pyrite-pseudomorphic goethite commonly comprised of the early and late phase. Furthermore, the stronger oxidation is related to more late goethite with more Al and Si content. The ubiquitous dolomite buffer kept the pH of the pore fluid neutral, resulting in the precipitation and accumulation of a goethite coating around pyrite, which further reduced the oxidation rate and formed pyrite-pseudomorphic goethite ultimately via coupled dissolution-reprecipitation reactions. In addition, the different mineralogical properties resulted in the differential oxidation of pyrite such that the smaller grains oxidized faster, and As within the pyrite accelerated the oxidation. Moreover, the rate-limited oxidation of pyrite under carbonate buffer prevents acid mine drainage (AMD) from forming and limits As release from arsenian pyrite into the external environment.\u0000 \u0000 Supplementary material:\u0000 https://doi.org/10.6084/m9.figshare.c.6267700\u0000","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42798020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The inheritance and migration characters of different elements in the soil profile can serve agriculture, environmental protection, mineral exploration, and other related research fields. These characters are mainly determined by the various geochemical behaviors of different elements. However, the elemental behaviors for the same element might be varied wildly due to various physical, chemical, and biological environments in local places. Because the transformation of rock to soil includes rock-weathering and pedogenic processes. Here, we propose a framework for evaluating the elemental inheritance and migration characters from the underlying rocks or C horizons during rock-weathering and pedogenic processes in local places. In this framework, random forest regression was used to evaluate the importance of controlling factors, i.e., parent material or rock, climate, vegetation, and topography, for individual elements during both processes. Further hierarchical clustering analysis was used to group elements into three inheritance categories according to their controlling factors. The framework was then used in the Daliangshan area. At last, we give some suggestions on the assessment of mineral resources, environment, and agriculture according to the elemental inheritance categories in the Daliangshan area. The methodological approach can be transferred to other local places. Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis Supplementary material: https://doi.org/10.6084/m9.figshare.c.6274063
{"title":"Elemental inheritance evaluation for geochemical elements in soil of the Daliangshan, China","authors":"Zhen-Jie Zhang, Hong Liu, Y. Ouyang","doi":"10.1144/geochem2022-010","DOIUrl":"https://doi.org/10.1144/geochem2022-010","url":null,"abstract":"The inheritance and migration characters of different elements in the soil profile can serve agriculture, environmental protection, mineral exploration, and other related research fields. These characters are mainly determined by the various geochemical behaviors of different elements. However, the elemental behaviors for the same element might be varied wildly due to various physical, chemical, and biological environments in local places. Because the transformation of rock to soil includes rock-weathering and pedogenic processes. Here, we propose a framework for evaluating the elemental inheritance and migration characters from the underlying rocks or C horizons during rock-weathering and pedogenic processes in local places. In this framework, random forest regression was used to evaluate the importance of controlling factors, i.e., parent material or rock, climate, vegetation, and topography, for individual elements during both processes. Further hierarchical clustering analysis was used to group elements into three inheritance categories according to their controlling factors. The framework was then used in the Daliangshan area. At last, we give some suggestions on the assessment of mineral resources, environment, and agriculture according to the elemental inheritance categories in the Daliangshan area. The methodological approach can be transferred to other local places.\u0000 \u0000 Thematic collection:\u0000 This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at:\u0000 https://www.lyellcollection.org/topic/collections/applications-of-innovations-in-geochemical-data-analysis\u0000 \u0000 \u0000 Supplementary material:\u0000 https://doi.org/10.6084/m9.figshare.c.6274063\u0000","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43700672","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
From 2007 to 2010, the U.S. Geological Survey (USGS) conducted a low-density (1 site per 1,600 km 2 ), soil geochemical and mineralogical survey of the conterminous United States (US) (approximately 8 million km 2 ). This project was initiated to address the lack of a national soil geochemical database that was a critical need for state and Federal environmental agencies, public health specialists, and those engaged in risk assessment of contaminated land. Sampling and analytical protocols were developed in consultation with stakeholders at a 2003 workshop and pilot studies were carried out from 2004 to 2007. Sampling began in 2007 and concluded in 2010. Chemical and mineralogical analyses were completed in 2013, and the data sets were released to the public that same year. Geochemical and mineralogical maps were published in 2014, and an interactive website was released in 2019. The author was Project Chief for this effort throughout the lifetime of the study. The evolution of the project is discussed from its inception through the publication of results and its impact. The lessons learned during the project are reviewed in the hope that applied geochemists who undertake such broad-scale geochemical mapping projects in the future will find them useful. Thematic collection: This article is part of the Continental-scale geochemical mapping collection available at: https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping
{"title":"Soil geochemical and mineralogical survey of the conterminous United States: a project retrospective","authors":"David B. Smith","doi":"10.1144/geochem2022-031","DOIUrl":"https://doi.org/10.1144/geochem2022-031","url":null,"abstract":"\u0000 From 2007 to 2010, the U.S. Geological Survey (USGS) conducted a low-density (1 site per 1,600 km\u0000 2\u0000 ), soil geochemical and mineralogical survey of the conterminous United States (US) (approximately 8 million km\u0000 2\u0000 ). This project was initiated to address the lack of a national soil geochemical database that was a critical need for state and Federal environmental agencies, public health specialists, and those engaged in risk assessment of contaminated land. Sampling and analytical protocols were developed in consultation with stakeholders at a 2003 workshop and pilot studies were carried out from 2004 to 2007. Sampling began in 2007 and concluded in 2010. Chemical and mineralogical analyses were completed in 2013, and the data sets were released to the public that same year. Geochemical and mineralogical maps were published in 2014, and an interactive website was released in 2019. The author was Project Chief for this effort throughout the lifetime of the study. The evolution of the project is discussed from its inception through the publication of results and its impact. The lessons learned during the project are reviewed in the hope that applied geochemists who undertake such broad-scale geochemical mapping projects in the future will find them useful.\u0000 \u0000 \u0000 Thematic collection:\u0000 This article is part of the Continental-scale geochemical mapping collection available at:\u0000 https://www.lyellcollection.org/topic/collections/continental-scale-geochemical-mapping\u0000","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45441430","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improvement of geochemical prospectivity mapping using power spectrum – area fractal modeling of multi-element mineralization factor (SAF-MF)","authors":"M. Seyedrahimi-Niaraq, H. Mahdiyanfar","doi":"10.1144/geochem2022-015","DOIUrl":"https://doi.org/10.1144/geochem2022-015","url":null,"abstract":"","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42006273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yuepeng Zhang, Xiaofeng Ye, S. Xie, Xiaoying Zhou, S. F. Awadelseid, Oraphan Yaisamut, Fanxing Meng
Extensive research has been conducted to evaluate mineral resource potential based on geochemical data, but this work is still challenging due to the existence of multiple evaluation solutions based on different methods. In this paper, we combine the multifractal analysis method with typical multivariate statistical methods to analyse the spatial characteristics of geochemical stream sediment data, aiming to quantitatively study the ore-forming potential of the elements in the central Kunlun area of Xinjiang, China. An R-type cluster analysis, Pearson correlation analysis, and principal component analysis are used to explore the correlations among the 12 target elements. The multifractal model is constructed by using the method of moments to analyse the spatial distribution patterns of the elements, and corresponding multifractal parameters are extracted to quantitatively describe their ore-forming strengths in the study area. The results show that Co, V, Ti, Fe2O3, MgO, and Cu compose a group of elements closely related to the regional geological background, while Pb, Zn, Bi, Sn, Au, and Ba are potential metallogenic elements with relatively high ore-forming strengths and favourable ore-forming potential. Multifractal theory further validates and evaluates the favourable ore-forming element group obtained through conventional geochemical multivariate statistical methods, thus providing a new idea for small-scale geochemical prospecting.Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis
{"title":"Implication of multifractal for quantitative evaluation of mineral resources in the central Kunlun area, Xinjiang, China","authors":"Yuepeng Zhang, Xiaofeng Ye, S. Xie, Xiaoying Zhou, S. F. Awadelseid, Oraphan Yaisamut, Fanxing Meng","doi":"10.1144/geochem2021-083","DOIUrl":"https://doi.org/10.1144/geochem2021-083","url":null,"abstract":"Extensive research has been conducted to evaluate mineral resource potential based on geochemical data, but this work is still challenging due to the existence of multiple evaluation solutions based on different methods. In this paper, we combine the multifractal analysis method with typical multivariate statistical methods to analyse the spatial characteristics of geochemical stream sediment data, aiming to quantitatively study the ore-forming potential of the elements in the central Kunlun area of Xinjiang, China. An R-type cluster analysis, Pearson correlation analysis, and principal component analysis are used to explore the correlations among the 12 target elements. The multifractal model is constructed by using the method of moments to analyse the spatial distribution patterns of the elements, and corresponding multifractal parameters are extracted to quantitatively describe their ore-forming strengths in the study area. The results show that Co, V, Ti, Fe2O3, MgO, and Cu compose a group of elements closely related to the regional geological background, while Pb, Zn, Bi, Sn, Au, and Ba are potential metallogenic elements with relatively high ore-forming strengths and favourable ore-forming potential. Multifractal theory further validates and evaluates the favourable ore-forming element group obtained through conventional geochemical multivariate statistical methods, thus providing a new idea for small-scale geochemical prospecting.Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysis","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49070350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Balance analysis of two groups of parts within a whole has become an important method for compositional data analysis. A compositional balance is a particular orthonormal coordinate that is depicted by the log-ratio between two groups of components. Two available approaches to compositional balance analysis (CoBA) can be adopted to generate targeted balances for geochemical pattern analysis and anomaly identification, so-called data-driven CoBA and knowledge-driven CoBA. For the data-driven CoBA, the balance is produced strictly by the rules of sequential binary partition (SBP), while for the knowledge-driven CoBA, the first group within a balance is composed of the interesting parts of the whole and the second group is defined by the remaining parts of the whole. Commonly, it is difficult to conceptualize balances, particularly for high-dimensional data, because it will produce a large number of orthonormal bases or balances based on CoBA. For a certain geochemical pattern, it might be represented by multiple compositional balances generated by data-driven and knowledge-driven CoBA. Thus, how to determine an optimal balance for geochemical pattern analysis and anomaly identification needs to be further explored. In the present study, this question was thoroughly investigated based on a case study from the Chinese Western Tianshan (CWT) region. Fourteen compositional balances and three principal factors associated with different geochemical patterns including gold and copper mineralization, and particular lithological units were selected for comparative studies to illustrate how to determine the optimal balances from the perspective of CoBA and multivariate statistical analysis.Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysisSupplementary material:https://doi.org/10.6084/m9.figshare.c.6083724
{"title":"How to determine the optimal balance for geochemical pattern recognition and anomaly mapping based on compositional balance analysis?","authors":"Yue Liu","doi":"10.1144/geochem2022-009","DOIUrl":"https://doi.org/10.1144/geochem2022-009","url":null,"abstract":"Balance analysis of two groups of parts within a whole has become an important method for compositional data analysis. A compositional balance is a particular orthonormal coordinate that is depicted by the log-ratio between two groups of components. Two available approaches to compositional balance analysis (CoBA) can be adopted to generate targeted balances for geochemical pattern analysis and anomaly identification, so-called data-driven CoBA and knowledge-driven CoBA. For the data-driven CoBA, the balance is produced strictly by the rules of sequential binary partition (SBP), while for the knowledge-driven CoBA, the first group within a balance is composed of the interesting parts of the whole and the second group is defined by the remaining parts of the whole. Commonly, it is difficult to conceptualize balances, particularly for high-dimensional data, because it will produce a large number of orthonormal bases or balances based on CoBA. For a certain geochemical pattern, it might be represented by multiple compositional balances generated by data-driven and knowledge-driven CoBA. Thus, how to determine an optimal balance for geochemical pattern analysis and anomaly identification needs to be further explored. In the present study, this question was thoroughly investigated based on a case study from the Chinese Western Tianshan (CWT) region. Fourteen compositional balances and three principal factors associated with different geochemical patterns including gold and copper mineralization, and particular lithological units were selected for comparative studies to illustrate how to determine the optimal balances from the perspective of CoBA and multivariate statistical analysis.Thematic collection: This article is part of the Applications of Innovations in Geochemical Data Analysis collection available at: https://www.lyellcollection.org/cc/applications-of-innovations-in-geochemical-data-analysisSupplementary material:https://doi.org/10.6084/m9.figshare.c.6083724","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46761292","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In geochemical exploration, a geochemical anomaly detection model is usually established to describe the population distribution of geochemical data, and samples that do not conform to the model are identified as geochemical anomalies. Because the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, the performance of geochemical anomaly detection model for mineral exploration targeting is affected to a certain extent. For this reason, neighborhood component analysis and dictionary learning algorithms were combined to detect geochemical anomalies associated with gold mineralization in the Chengde area in Hebei Province, China. Neighborhood component analysis was used to transform geochemical data from the input space into the neighborhood component space to enhance the separability between the geochemical anomalies associated with gold mineralization and the background. Dictionary learning models for geochemical anomaly detection were established in the neighborhood component space. The performance of the dictionary learning models established in the neighborhood component space was compared with that of the corresponding models established in the input space in geochemical anomaly detection. The results show that the dictionary learning models established in the neighborhood component space are superior to the corresponding models established in the input space in geochemical anomaly detection. In addition, there is a strong consistency between the mineral exploration targeting results and metallogenic characteristics of the study area. Therefore, combining neighborhood component analysis and dictionary learning algorithms can improve the performance of the dictionary learning models in geochemical anomaly detection.
{"title":"Combining neighborhood component analysis with dictionary learning algorithms to improve the performance of the dictionary learning models for geochemical anomaly detection","authors":"Yongliang Chen, Alina Shayilan","doi":"10.1144/geochem2022-016","DOIUrl":"https://doi.org/10.1144/geochem2022-016","url":null,"abstract":"In geochemical exploration, a geochemical anomaly detection model is usually established to describe the population distribution of geochemical data, and samples that do not conform to the model are identified as geochemical anomalies. Because the establishment of a geochemical anomaly detection model does not make use of the relationship between geochemical elements and mineralization, the performance of geochemical anomaly detection model for mineral exploration targeting is affected to a certain extent. For this reason, neighborhood component analysis and dictionary learning algorithms were combined to detect geochemical anomalies associated with gold mineralization in the Chengde area in Hebei Province, China. Neighborhood component analysis was used to transform geochemical data from the input space into the neighborhood component space to enhance the separability between the geochemical anomalies associated with gold mineralization and the background. Dictionary learning models for geochemical anomaly detection were established in the neighborhood component space. The performance of the dictionary learning models established in the neighborhood component space was compared with that of the corresponding models established in the input space in geochemical anomaly detection. The results show that the dictionary learning models established in the neighborhood component space are superior to the corresponding models established in the input space in geochemical anomaly detection. In addition, there is a strong consistency between the mineral exploration targeting results and metallogenic characteristics of the study area. Therefore, combining neighborhood component analysis and dictionary learning algorithms can improve the performance of the dictionary learning models in geochemical anomaly detection.","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41917068","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fereshteh Khammar, Shahab Alborzian Joonaghani, Leila Jan Abadi, M. Boomeri, D. Lentz
This exploration methodology case study is situated along the Sistan Suture Zone where the granitoid suite is a mantle-derived multiphase intrusive complex. One of the characteristics of this region is the presence of large areas of sulfide-bearing, quartz-rich stockwork and pyritic veins. Geochemical findings show it is limited to the calc-alkaline to shoshonitic series intrusions and is associated with a volcanic arc (I-type) formed within an active continental margin subduction setting. The associated intrusive complex has characteristics consistent with Cu productive porphyries, supported by high K-adakitic Sr/Y, La/Yb, Y, and Al2O3/TiO2 geochemical signatures. The stockwork mineralization includes the hypogene (chalcopyrite and bornite), with locally superimposed supergene (covellite, malachite, goethite, and hematite) zones. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (visible-near infrared (VNIR) and shortwave infrared (SWIR) bands) and Mixture Tuned Matched Filtering (MTMF) algorithm, enable identification of each hydrothermal alteration type, especially where pictures were innovatively classified by a concentration-number (C-N) fractal method. Four alteration types are evident, i.e. phyllic, argillic, and propylitic, as well as secondary (supergene) jarositethat are associated with gossans, which are an indicator of the hypogene pyritic shell. The propylitic alteration envelopes the phyllic and argillic varieties, forming a belt around the pyritic shell; alteration assemblages were confirmed by XRD analysis. Finally, all results show a mineralization-alteration pattern within this case study region that is similar to those of known porphyry copper and associated molybdenum- and gold-bearing systems in this region of Iran and worldwide.
{"title":"Investigation of geochemical characteristics and hydrothermal alteration zone mapping supported by remote sensing of a porphyry-copper prospect, southeastern Iran: integrated applications to exploration","authors":"Fereshteh Khammar, Shahab Alborzian Joonaghani, Leila Jan Abadi, M. Boomeri, D. Lentz","doi":"10.1144/geochem2022-014","DOIUrl":"https://doi.org/10.1144/geochem2022-014","url":null,"abstract":"This exploration methodology case study is situated along the Sistan Suture Zone where the granitoid suite is a mantle-derived multiphase intrusive complex. One of the characteristics of this region is the presence of large areas of sulfide-bearing, quartz-rich stockwork and pyritic veins. Geochemical findings show it is limited to the calc-alkaline to shoshonitic series intrusions and is associated with a volcanic arc (I-type) formed within an active continental margin subduction setting. The associated intrusive complex has characteristics consistent with Cu productive porphyries, supported by high K-adakitic Sr/Y, La/Yb, Y, and Al2O3/TiO2 geochemical signatures. The stockwork mineralization includes the hypogene (chalcopyrite and bornite), with locally superimposed supergene (covellite, malachite, goethite, and hematite) zones. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images (visible-near infrared (VNIR) and shortwave infrared (SWIR) bands) and Mixture Tuned Matched Filtering (MTMF) algorithm, enable identification of each hydrothermal alteration type, especially where pictures were innovatively classified by a concentration-number (C-N) fractal method. Four alteration types are evident, i.e. phyllic, argillic, and propylitic, as well as secondary (supergene) jarositethat are associated with gossans, which are an indicator of the hypogene pyritic shell. The propylitic alteration envelopes the phyllic and argillic varieties, forming a belt around the pyritic shell; alteration assemblages were confirmed by XRD analysis. Finally, all results show a mineralization-alteration pattern within this case study region that is similar to those of known porphyry copper and associated molybdenum- and gold-bearing systems in this region of Iran and worldwide.","PeriodicalId":55114,"journal":{"name":"Geochemistry-Exploration Environment Analysis","volume":" ","pages":""},"PeriodicalIF":2.4,"publicationDate":"2022-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43856083","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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