After long-term development, mathematical geology has today become an independent discipline. Big Data science, which has become a new scientific paradigm in the 21st century, gives rise to the geological Big Data, i....After long-term development, mathematical geology has today become an independent discipline. Big Data science, which has become a new scientific paradigm in the 21st century, gives rise to the geological Big Data, i.e. mathematical geology and quantitative geoscience. Thanks to a robust macro strategy for big data, China＇s quantitative geoscience and geological big data＇s rapid development meets present requirements and has kept up with international levels. This paper presents China＇s decade-long achievements in quantitative prediction and assessment of mineral resources, geoscience information and software systems, geological information platform development, etc., with an emphasis on application of geological big data in informatics, quantitative mineral prediction, geological environment and disaster management, digital land survey, digital city, etc. Looking ahead, mathematical geology is moving towards ＂Digital Geology＂, ＂Digital Land＂ and ＂Geological Cloud＂, eventually realizing China＇s grand ＂Digital China＂ blueprint, and these valuable results will be showcased on the international academic arena.展开更多
Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was ...Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was established by using the exploration data of the deposit and related regions in this area, so as to determine the prospecting prospect area in the study area. In this paper, the Jacknife analysis module of maximum entropy model is used to quantitatively rank the importance of 39 geochemical element variables, and finally obtain the prospecting prospect map of the study area. The research results show that the Dapingzhang mining area has the potential to find hidden ore in the deep and surrounding areas, and the northern and southern ends and western sides of the rock ore control structural belt in the eastern region of the mining area have good prospecting prospects. The research results provide an important basis for the deployment of follow-up exploration work in the study area, and the maximum entropy model has a good application effect in mineral resources exploration.展开更多
The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt,which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt.At present,there is...The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt,which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt.At present,there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt.One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear.Due to the diversity of volcanic rock geochemical characteristics and its related interpretations,there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt:island arc and continental rift.In recent years,it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt.This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia,Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt.Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt,and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt.The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1,which indicates that the island arc tectonic setting is more credible.In this paper,it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction,while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage.This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east.Therefore,the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.展开更多
The Geza arc is an important part of the Sanjiang tectono-magmatic belt and is a newly discovered copper polymetallic ore concentration area in northwest Yunnan province,Southwest China.The area comprises numerous met...The Geza arc is an important part of the Sanjiang tectono-magmatic belt and is a newly discovered copper polymetallic ore concentration area in northwest Yunnan province,Southwest China.The area comprises numerous metal ore deposits,including one super-largedeposit,three large deposits,etc.The formation of these deposits was closely related to intermediate–acidic magmatic intrusions.Based on previous studies,the“big data”analysis technique was used for a comparative study of large geochemical datasets of granite related to ore-formation in the Geza porphyry copper deposit and global adakites.As a result,1313 element combinations and 127,765 overlap ratios were obtained.The results show that the Geza porphyry has similar geochemical characteristics to global adakites(the ratios of REE and Ga to major elements are in the range of global adakites).However,theCu,Mo,and Zn contents of the porphyry are significantly higher than those of global adakites,and the porphyry may,therefore,represent an end-member of the global range of adakite composition.In addition,the geochemistry of adakites associated with the porphyry copper deposits overlaps in part with that of global adakites,although most of the data lie outside of the range of global adakites(i.e.lowMn/Cu,Sr/Cu,Na/Cu,and Zr/Cu values,and high Th/Cu,Ba/Cu,Na/Mo,Rb/Mo,Th/Mo,Ta/Mo,Ba/Mo,Mn/Zn,and Ba/Znvalues).The samples with characteristics that deviate significantly from the geochemistry of global adakites show more advanced mineralization and alteration,and a stronger relationship with Cu and Momineralization.The results of geochemical data mining can be used as a prospecting indicator,and provide a new scientific basis for geological prospecting of the deep levels and per-iphery of the Geza Cu polymetallic ore belt.展开更多
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 effectiv...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.展开更多
文摘After long-term development, mathematical geology has today become an independent discipline. Big Data science, which has become a new scientific paradigm in the 21st century, gives rise to the geological Big Data, i.e. mathematical geology and quantitative geoscience. Thanks to a robust macro strategy for big data, China＇s quantitative geoscience and geological big data＇s rapid development meets present requirements and has kept up with international levels. This paper presents China＇s decade-long achievements in quantitative prediction and assessment of mineral resources, geoscience information and software systems, geological information platform development, etc., with an emphasis on application of geological big data in informatics, quantitative mineral prediction, geological environment and disaster management, digital land survey, digital city, etc. Looking ahead, mathematical geology is moving towards ＂Digital Geology＂, ＂Digital Land＂ and ＂Geological Cloud＂, eventually realizing China＇s grand ＂Digital China＂ blueprint, and these valuable results will be showcased on the international academic arena.
文摘Taking the Dapingzhang copper-polymetallic deposit in Yunnan Province, China as the research object, the maximum entropy model was used to extract the mining information, and the mineral resource prediction model was established by using the exploration data of the deposit and related regions in this area, so as to determine the prospecting prospect area in the study area. In this paper, the Jacknife analysis module of maximum entropy model is used to quantitatively rank the importance of 39 geochemical element variables, and finally obtain the prospecting prospect map of the study area. The research results show that the Dapingzhang mining area has the potential to find hidden ore in the deep and surrounding areas, and the northern and southern ends and western sides of the rock ore control structural belt in the eastern region of the mining area have good prospecting prospects. The research results provide an important basis for the deployment of follow-up exploration work in the study area, and the maximum entropy model has a good application effect in mineral resources exploration.
文摘The Xingmeng orogenic belt is located in the eastern section of the Central Asian orogenic belt,which is one of the key areas to study the formation and evolution of the Central Asian orogenic belt.At present,there is a huge controversy over the closure time of the Paleo-Asian Ocean in the Xingmeng orogenic belt.One of the reasons is that the genetic tectonic setting of the Carboniferous volcanic rocks is not clear.Due to the diversity of volcanic rock geochemical characteristics and its related interpretations,there are two different views on the tectonic setting of Carboniferous volcanic rocks in the Xingmeng orogenic belt:island arc and continental rift.In recent years,it is one of the important development directions in the application of geological big data technology to analyze geochemical data based on machine learning methods and further infer the tectonic background of basalt.This paper systematically collects Carboniferous basic rock data from Dongwuqi area of Inner Mongolia,Keyouzhongqi area of Inner Mongolia and Beishan area in the southern section of the Central Asian Orogenic Belt.Random forest algorithm is used for training sets of major elements and trace elements in global island arc basalt and rift basalt,and then the trained model is used to predict the tectonic setting of the Carboniferous magmatic rock samples in the Xingmeng orogenic belt.The prediction results shows that the island arc probability of most of the research samples is between 0.65 and 1,which indicates that the island arc tectonic setting is more credible.In this paper,it is concluded that magmatism in the Beishan area of the southern part of the Central Asian Orogenic belt in the Early Carboniferous may have formed in the heyday of subduction,while the Xingmeng orogenic belt in the Late Carboniferous may have been in the late subduction stage to the collision or even the early rifting stage.This temporal and spatial evolution shows that the subduction of the Paleo-Asian Ocean is different from west to east.Therefore,the research results of this paper show that the subduction of the Xingmeng orogenic belt in the Carboniferous has not ended yet.
基金This paper is supported by the Key Laboratory of Geological Information Technology,Ministry of Natural Resources of the People’s Repbulic of China[Grant No:KKF0201821043]Natural Science Foundation of China[Grant No.41862009]+1 种基金Science and Technology Award of Yunnan Province-Outstanding Contribution award Project[Grant No.2007001]Key disciplines construction funding project of Kunming University of Science and Technology[1407839305].
文摘The Geza arc is an important part of the Sanjiang tectono-magmatic belt and is a newly discovered copper polymetallic ore concentration area in northwest Yunnan province,Southwest China.The area comprises numerous metal ore deposits,including one super-largedeposit,three large deposits,etc.The formation of these deposits was closely related to intermediate–acidic magmatic intrusions.Based on previous studies,the“big data”analysis technique was used for a comparative study of large geochemical datasets of granite related to ore-formation in the Geza porphyry copper deposit and global adakites.As a result,1313 element combinations and 127,765 overlap ratios were obtained.The results show that the Geza porphyry has similar geochemical characteristics to global adakites(the ratios of REE and Ga to major elements are in the range of global adakites).However,theCu,Mo,and Zn contents of the porphyry are significantly higher than those of global adakites,and the porphyry may,therefore,represent an end-member of the global range of adakite composition.In addition,the geochemistry of adakites associated with the porphyry copper deposits overlaps in part with that of global adakites,although most of the data lie outside of the range of global adakites(i.e.lowMn/Cu,Sr/Cu,Na/Cu,and Zr/Cu values,and high Th/Cu,Ba/Cu,Na/Mo,Rb/Mo,Th/Mo,Ta/Mo,Ba/Mo,Mn/Zn,and Ba/Znvalues).The samples with characteristics that deviate significantly from the geochemistry of global adakites show more advanced mineralization and alteration,and a stronger relationship with Cu and Momineralization.The results of geochemical data mining can be used as a prospecting indicator,and provide a new scientific basis for geological prospecting of the deep levels and per-iphery of the Geza Cu polymetallic ore belt.
基金supported by the National Natural Science Foundation of China(Grant Nos.42321001,42172326)the Natural Science Foundation of Hubei Province(China)(Grant No.2023AFA001)。
文摘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.
