Five gold deposits (mineralization) were found in the study area by means of geologi</span><span style="font-family:Verdana;">cal mapping, soil geochemical survey and trough exploration engineeri...Five gold deposits (mineralization) were found in the study area by means of geologi</span><span style="font-family:Verdana;">cal mapping, soil geochemical survey and trough exploration engineering. The</span><span style="font-family:Verdana;"> ore-bearing lithology is mainly metam</span></span><span style="font-family:Verdana;">orphic feldspar sandstone of </span><span style="font-family:Verdana;">the </span><span style="font-family:""><span style="font-family:Verdana;">Upper Carboniferous Benbatu Formation, and the gold (mineralization) body is controlled by both structural factors </span><span style="font-family:Verdana;">and stratigraphic factors of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">Upper Carboniferous Benbatu Formation. The genetic</span><span style="font-family:""><span style="font-family:Verdana;"> type is preliminary concluded to be volcanic hyd</span><span style="color:black;font-family:Verdana;">rothermal type, and the metallogenic age is late Variscan. In this paper, by studying the geological characteristics and metallogenic geological conditions of the gold orebody in the area, a regional prospecting model has been established, which is of great significance to better guide the prospecting work of similar gold deposits in the area and the region.展开更多
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 appropriat...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.展开更多
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,...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.展开更多
The Daheishan supergiant porphyry molybdenum deposit(also referred to as the Daheishan deposit)is the second largest molybdenum deposit in Asia and ranks fifth among the top seven molybdenum deposits globally with tot...The Daheishan supergiant porphyry molybdenum deposit(also referred to as the Daheishan deposit)is the second largest molybdenum deposit in Asia and ranks fifth among the top seven molybdenum deposits globally with total molybdenum reserves of 1.65 billion tons,an average molybdenum ore grade of 0.081%,and molybdenum resources of 1.09 million tons.The main ore body is housed in the granodiorite porphyry plutons and their surrounding inequigranular granodiorite plutons,with high-grade ores largely located in the ore-bearing granodiorite porphyries in the middle-upper part of the porphyry plutons.Specifically,it appears as an ore pipe with a large upper part and a small lower part,measuring about 1700 m in length and width,extending for about 500 m vertically,and covering an area of 2.3 km^(2).Mineralogically,the main ore body consists of molybdenite,chalcopyrite,and sphalerite horizontally from its center outward and exhibits molybdenite,azurite,and pyrite vertically from top to bottom.The primary ore minerals include pyrite and molybdenite,and the secondary ore minerals include sphalerite,chalcopyrite,tetrahedrite,and scheelite,with average grades of molybdenum,copper,sulfur,gallium,and rhenium being 0.081%,0.033%,1.67%,0.001%,and 0.0012%,respectively.The ore-forming fluids of the Daheishan deposit originated as the CO_(2)-H_(2)O-NaCl multiphase magmatic fluid system,rich in CO_(2)and bearing minor amounts of CH4,N2,and H2S,and later mixed with meteoric precipitation.In various mineralization stages,the ore-forming fluids had homogenization temperatures of>420℃‒400℃,360℃‒350℃,340℃‒230℃,220℃‒210℃,and 180℃‒160℃and salinities of>41.05%‒9.8%NaCleqv,38.16%‒4.48%NaCleqv,35.78%‒4.49%NaCleqv,7.43%NaCleqv,and 7.8%‒9.5%NaCleqv,respectively.The mineralization of the Daheishan deposit occurred at 186‒167 Ma.The granites closely related to the mineralization include granodiorites(granodiorite porphyries)and monzogranites(monzogranite porphyries),which were mineralized after magmatic evolution(189‒167 Ma).Moreover,these mineralization-related granites exhibit low initial strontium content and high initial neodymium content,indicating that these granites underwent crust-mantle mixing.The Daheishan deposit formed during the Early-Middle Jurassic,during which basaltic magma underplating induced the lower-crust melting,leading to the formation of magma chambers.After the fractional crystallization of magmas,ore-bearing fluids formed.As the temperature and pressure decreased,the ore-bearing fluids boiled drops while ascending,leading to massive unloading of metal elements.Consequently,brecciated and veinlet-disseminated ore bodies formed.展开更多
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 zone...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.展开更多
The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the ...The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.展开更多
文摘Five gold deposits (mineralization) were found in the study area by means of geologi</span><span style="font-family:Verdana;">cal mapping, soil geochemical survey and trough exploration engineering. The</span><span style="font-family:Verdana;"> ore-bearing lithology is mainly metam</span></span><span style="font-family:Verdana;">orphic feldspar sandstone of </span><span style="font-family:Verdana;">the </span><span style="font-family:""><span style="font-family:Verdana;">Upper Carboniferous Benbatu Formation, and the gold (mineralization) body is controlled by both structural factors </span><span style="font-family:Verdana;">and stratigraphic factors of </span></span><span style="font-family:Verdana;">the </span><span style="font-family:Verdana;">Upper Carboniferous Benbatu Formation. The genetic</span><span style="font-family:""><span style="font-family:Verdana;"> type is preliminary concluded to be volcanic hyd</span><span style="color:black;font-family:Verdana;">rothermal type, and the metallogenic age is late Variscan. In this paper, by studying the geological characteristics and metallogenic geological conditions of the gold orebody in the area, a regional prospecting model has been established, which is of great significance to better guide the prospecting work of similar gold deposits in the area and the region.
基金funded by the British Geological Survey,United Kingdom(S267)the Natural Environment Research Council(NERC),United Kingdom。
文摘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.
基金funded by a pilot project entitled“Deep Geological Survey of Benxi-Linjiang Area”(1212011220247)of the 3D Geological Mapping and Deep Geological Survey of China Geological Survey。
文摘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.
基金This study was jointly funded by a project of the National Natural Science Foundation of China(42102087)a project of the China Postdoctoral Science Foundation(2022M712966)a key special project of the Ministry of Science and Technology of China(2021QZKK0304).
文摘The Daheishan supergiant porphyry molybdenum deposit(also referred to as the Daheishan deposit)is the second largest molybdenum deposit in Asia and ranks fifth among the top seven molybdenum deposits globally with total molybdenum reserves of 1.65 billion tons,an average molybdenum ore grade of 0.081%,and molybdenum resources of 1.09 million tons.The main ore body is housed in the granodiorite porphyry plutons and their surrounding inequigranular granodiorite plutons,with high-grade ores largely located in the ore-bearing granodiorite porphyries in the middle-upper part of the porphyry plutons.Specifically,it appears as an ore pipe with a large upper part and a small lower part,measuring about 1700 m in length and width,extending for about 500 m vertically,and covering an area of 2.3 km^(2).Mineralogically,the main ore body consists of molybdenite,chalcopyrite,and sphalerite horizontally from its center outward and exhibits molybdenite,azurite,and pyrite vertically from top to bottom.The primary ore minerals include pyrite and molybdenite,and the secondary ore minerals include sphalerite,chalcopyrite,tetrahedrite,and scheelite,with average grades of molybdenum,copper,sulfur,gallium,and rhenium being 0.081%,0.033%,1.67%,0.001%,and 0.0012%,respectively.The ore-forming fluids of the Daheishan deposit originated as the CO_(2)-H_(2)O-NaCl multiphase magmatic fluid system,rich in CO_(2)and bearing minor amounts of CH4,N2,and H2S,and later mixed with meteoric precipitation.In various mineralization stages,the ore-forming fluids had homogenization temperatures of>420℃‒400℃,360℃‒350℃,340℃‒230℃,220℃‒210℃,and 180℃‒160℃and salinities of>41.05%‒9.8%NaCleqv,38.16%‒4.48%NaCleqv,35.78%‒4.49%NaCleqv,7.43%NaCleqv,and 7.8%‒9.5%NaCleqv,respectively.The mineralization of the Daheishan deposit occurred at 186‒167 Ma.The granites closely related to the mineralization include granodiorites(granodiorite porphyries)and monzogranites(monzogranite porphyries),which were mineralized after magmatic evolution(189‒167 Ma).Moreover,these mineralization-related granites exhibit low initial strontium content and high initial neodymium content,indicating that these granites underwent crust-mantle mixing.The Daheishan deposit formed during the Early-Middle Jurassic,during which basaltic magma underplating induced the lower-crust melting,leading to the formation of magma chambers.After the fractional crystallization of magmas,ore-bearing fluids formed.As the temperature and pressure decreased,the ore-bearing fluids boiled drops while ascending,leading to massive unloading of metal elements.Consequently,brecciated and veinlet-disseminated ore bodies formed.
基金funded by the National Natural Science Foundation of China(Nos.41972309,42272344,42030809,42072325,72088101)National Key R&D Program of China(No.2019YFC1805905).
文摘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.
基金supported by the National Natural Science Foundation of China(42001314)the Open Research Fund of the State Laboratory of Information Engineering in Surveying,Mapping,and Remote Sensing,Wuhan University(grant number 20R02)+1 种基金Torbern Tagesson was additionally funded by the Swedish National Space Agency(SNSA 2021-00144)FORMAS(Dnr.2021-00644).
文摘The ratio of leaf carotenoid to chlorophyll(Car/Chl)is an indicator of vegetation photosynthesis,development and responses to stress.However,the correlation between Car and Chl,and their overlapping absorption in the visible spectral domain pose a challenge for optical remote sensing of their ratio.This study aims to investigate combinations of vegetation indices(VIs)to minimize the influence of Car-Chl correlation,thus being more sensitive to the variability in the ratio across vegetation species and sites.VIs sensitive to Car and Chl variability were combined into four candidates of combinations,using a simulated dataset from the PROSPECT model.The VI combinations were then tested using six simulated datasets with different Car-Chl correlations,and evaluated against four independent datasets.The ratio of the carotenoid triangle ratio index(CTRI)with the red-edge chlorophyll index(CIred-edge)was found least influenced by the Car-Chl correlation and demonstrated a superior ability for estimating Car/Chl variability.Compared with published VIs and two machine learning algorithms,CTRI/CIred-edge also showed the optimal performance in the fourfield datasets.This new VI combination could be useful to provide insights in spatiotemporal variability in the leaf Car/Chl ratio,applicable for assessing vegetation physiology,phenology,and response to environmental stress.
