While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geolo...While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.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
The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-el...The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.展开更多
Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the ev...Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.展开更多
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
Using prospecting line profile map in combination with drilling and other information for 3D reconstruction of geological model is an important method of 3D geological modeling.?This paper?discusses the theory and imp...Using prospecting line profile map in combination with drilling and other information for 3D reconstruction of geological model is an important method of 3D geological modeling.?This paper?discusses the theory and implementation method of 2D prospecting line map into 3D prospecting line map and then into 3D model. The authors propose that it needs twice upgrading dimension to reconstruction 3D geology model from prospecting line profile map. The first upgrading dimension is to convert profile from 2D into 3D profile,?i.e.?the 2D points in the 2D profile map upgrading dimensional transformation to 3D points in a 3D profile. The second upgrading dimension is that transform 0D point 1D curve and 2D polygon feature into 1D curve, 2D surface and 3D solid feature. The paper reexamines contents and forms in prospecting line map from the two different viewpoints of geology and geographic information science. The process of 3D geology modeling from 2D prospecting map is summarized as follows. Firstly, profile is divided into several sections by beginning, end and drill point of the prospecting line. Next, a 3D folded upright profile frame is built by 2D folded prospecting line on the plan map. Then, 2D points of features on 2D profile are converted into 3D points on 3D profile section by section. And then, adding switch control points for the long line crossover two segments. Lastly, 1D curve features are upgraded to 2D surface.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
基金Project(2017YFC0601503)supported by the National Key R&D Program of ChinaProjects(41772349,41972309,41472301,41772348)supported by the National Natural Science Foundation of China。
文摘While the region of western Guangxi-southeastern Yunan, China, is known and considered prospective for manganese deposits, carrying out prospectivity mapping in this region is challenging due to the diversity of geological factors, the complexity of geological process and the asymmetry of geo-information. In this work, the manganese potential mapping for further exploration targeting is implemented via spatial analysis and modal-adaptive prospectivity modeling. On the basis of targeting criteria developed by the mineral system approach, the spatial analysis is leveraged to extract the predictor variables to identify features of the geological process. Specifically, a metallogenic field analysis approach is proposed to extract metallogenic information that quantifies the regional impacts of the synsedimentary faults and sedimentary basins. In the integration of the extracted predictor variables, a modal-adaptive prospectivity model is built, which allows to adapt different data availability and geological process. The resulting prospective areas of high potential not only correspond to the areas of known manganese deposits but also provide a number of favorable targets in the region for future mineral exploration.
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.
文摘The Varzaghan district at the northwestern margin of the Urumieh–Dokhtar magmatic arc, is considered a promising area for the exploration of porphyry Cu deposits in Iran. In this study we identified mono-and multi-element geochemical anomalies associated with Cu–Au–Mo–Bi mineralization in the central parts of the Varzaghan district by applying the concentration–area fractal method. After mono-element geochemical investigations, principal component analysis was applied to ten selected elements in order to acquire a multi-element geochemical signature based on the mineralization-related component. Quantitative comparisons of the obtained fractal-based populations were carried out in accordance with known Cu occurrences using Student's t-values. Then,significant mono-and multi-element geochemical layers were separately combined with related geologic and structural layers to generate prospectivity models, using the fuzzy GAMMA approach. For quantitative evaluation of the effectiveness of different geochemical signatures in final prospectivity models, a prediction-area plot was adapted. The results show that the multi-element geochemical signature of principal component one(PC1) is more effective than mono-element layers in delimiting exploration targets related to porphyry Cu deposits.
基金supported by the Geological Society of Australia(Honours Endowment Fund)the Australian Institute of Geoscientists(Honours Bursary)by ARC LP140100267
文摘Prospectivity analyses are used to reduce the exploration search space for locating areas prospective for mineral deposits.The scale of a study and the type of mineral system associated with the deposit control the evidence layers used as proxies that represent critical ore genesis processes.In particular,knowledge-driven approaches(fuzzy logic)use a conceptual mineral systems model from which data proxies represent the critical components.These typically vary based on the scale of study and the type of mineral system being predicted.Prospectivity analyses utilising interpreted data to represent proxies for a mineral system model inherit the subjectivity of the interpretations and the uncertainties of the evidence layers used in the model.In the case study presented,the prospectivity for remobilised nickel sulphide(NiS)in the west Kimberley,Western Australia,is assessed with two novel techniques that objectively grade interpretations and accommodate alternative mineralisation scenarios.Exploration targets are then identified and supplied with a robustness assessment that reflects the variability of prospectivity value for each location when all models are considered.The first technique grades the strength of structural interpretations on an individual line-segment basis.Gradings are obtained from an objective measure of feature evidence,which is the quantification of specific patterns in geophysical data that are considered to reveal underlying structure.Individual structures are weighted in the prospectivity model with grading values correlated to their feature evidence.This technique allows interpreted features to contribute prospectivity proportional to their strength in feature evidence and indicates the level of associated stochastic uncertainty.The second technique aims to embrace the systemic uncertainty of modelling complex mineral systems.In this approach,multiple prospectivity maps are each generated with different combinations of confidence values applied to evidence layers to represent the diversity of processes potentially leading to ore deposition.With a suite of prospectivity maps,the most robust exploration targets are the locations with the highest prospectivity values showing the smallest range amongst the model suite.This new technique offers an approach that reveals to the modeller a range of alternative mineralisation scenarios while employing a sensible mineral systems model,robust modelling of prospectivity and significantly reducing the exploration search space for Ni.
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.
文摘Using prospecting line profile map in combination with drilling and other information for 3D reconstruction of geological model is an important method of 3D geological modeling.?This paper?discusses the theory and implementation method of 2D prospecting line map into 3D prospecting line map and then into 3D model. The authors propose that it needs twice upgrading dimension to reconstruction 3D geology model from prospecting line profile map. The first upgrading dimension is to convert profile from 2D into 3D profile,?i.e.?the 2D points in the 2D profile map upgrading dimensional transformation to 3D points in a 3D profile. The second upgrading dimension is that transform 0D point 1D curve and 2D polygon feature into 1D curve, 2D surface and 3D solid feature. The paper reexamines contents and forms in prospecting line map from the two different viewpoints of geology and geographic information science. The process of 3D geology modeling from 2D prospecting map is summarized as follows. Firstly, profile is divided into several sections by beginning, end and drill point of the prospecting line. Next, a 3D folded upright profile frame is built by 2D folded prospecting line on the plan map. Then, 2D points of features on 2D profile are converted into 3D points on 3D profile section by section. And then, adding switch control points for the long line crossover two segments. Lastly, 1D curve features are upgraded to 2D surface.
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.
