Analyzing the information carriede by seismic waves is a major means for human beings to have an insight into the structure of the earth’s interior,and by using artificial seismic sources to excite seismic waves,we c...Analyzing the information carriede by seismic waves is a major means for human beings to have an insight into the structure of the earth’s interior,and by using artificial seismic sources to excite seismic waves,we can obtain high-resolution images for the crustal and smaller scale medium.Artificial seismic exploration methods have been widely applied to fields such as展开更多
Academician Dai Jinxing has long been engaged in natural gas geology and geochemical researches,and has made prominent contributions to the establishment and development of China’s theory of coal-derived gas.He has o...Academician Dai Jinxing has long been engaged in natural gas geology and geochemical researches,and has made prominent contributions to the establishment and development of China’s theory of coal-derived gas.He has opened up new areas of coal-derived gas exploration,natural gas formation theory and formation“ control conditions of large ” medium gas fields.展开更多
In exploration geochemistry,advances in the detection limit,breadth of elements analyze-able,accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in g...In exploration geochemistry,advances in the detection limit,breadth of elements analyze-able,accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas.While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data,especially where modern data is considerably different than legacy data,it is an expensive exercise.The risk associated with modernizing such legacy data lies within its uncertainty in return(e.g.,the possibility of new discoveries,in primarily greenfield settings).Without any advanced knowledge of yet unanalyzed elements,the importance of re-analyses remains ambiguous.To address this uncertainty,we apply machine learning to multivariate geochemical data from different regions in Canada(i.e.,the Churchill Province and the Trans-Hudson Orogen)in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses.Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data(e.g.,prospectivity mapping).Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.展开更多
文摘Analyzing the information carriede by seismic waves is a major means for human beings to have an insight into the structure of the earth’s interior,and by using artificial seismic sources to excite seismic waves,we can obtain high-resolution images for the crustal and smaller scale medium.Artificial seismic exploration methods have been widely applied to fields such as
文摘Academician Dai Jinxing has long been engaged in natural gas geology and geochemical researches,and has made prominent contributions to the establishment and development of China’s theory of coal-derived gas.He has opened up new areas of coal-derived gas exploration,natural gas formation theory and formation“ control conditions of large ” medium gas fields.
基金Supported by a Department of Science and Innovation(DSI)-National Research Foundation(NRF)Thuthuka Grant(Grant UID:121973),and DSI-NRF CIMERA.
文摘In exploration geochemistry,advances in the detection limit,breadth of elements analyze-able,accuracy and precision of analytical instruments have motivated the re-analysis of legacy samples to improve confidence in geochemical data and gain more insights into potentially mineralized areas.While a re-analysis campaign in a geochemical exploration program modernizes legacy geochemical data by providing more trustworthy and higher-dimensional geochemical data,especially where modern data is considerably different than legacy data,it is an expensive exercise.The risk associated with modernizing such legacy data lies within its uncertainty in return(e.g.,the possibility of new discoveries,in primarily greenfield settings).Without any advanced knowledge of yet unanalyzed elements,the importance of re-analyses remains ambiguous.To address this uncertainty,we apply machine learning to multivariate geochemical data from different regions in Canada(i.e.,the Churchill Province and the Trans-Hudson Orogen)in order to use legacy geochemical data to predict modern and higher dimensional multi-elemental concentrations ahead of planned re-analyses.Our study demonstrates that legacy and modern geochemical data can be repurposed to predict yet unanalyzed elements that will be realized from re-analyses and in a manner that significantly reduces the latency to downstream usage of modern geochemical data(e.g.,prospectivity mapping).Findings from this study serve as a pillar of a framework for exploration geologists to predictively explore and prioritize potentially mineralized districts for further prospects in a timely manner before employing more invasive and expensive techniques.
