Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)depo...Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe 展开更多
Most known mineral deposits were discovered by accident using expensive,time-consuming,and knowledgebased methods such as stream sediment geochemical data,diamond drilling,reconnaissance geochemical and geophysical su...Most known mineral deposits were discovered by accident using expensive,time-consuming,and knowledgebased methods such as stream sediment geochemical data,diamond drilling,reconnaissance geochemical and geophysical surveys,and/or remote sensing.Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials,prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration.Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost.Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation.In this study,we extend an artificial intelligence-based,unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager(OLI)satellite imagery and machine learning.The novelty in our method includes:(1)knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures;(2)detection of anomalies occurs only in the variable domain;and(3)a choice of a range of machine learning algorithms to balance between explain-ability and performance.Our new unsupervised method detects anomalies through three successive stages,namely(a)stage Ⅰ–acquisition of satellite imagery,data processing and selection of bands,(b)stage Ⅱ–predictive modelling and anomaly detection,and(c)stage Ⅲ–construction of anomaly maps and analysis.In this study,the new method was tested over the Assen iron deposit in the Transvaal Supergroup(South Africa).It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known.To summarise the anomalies in the area,principal component analysis was used on the reconstruction errors across all modelled bands.Our method enhanced the Assen deposit as an anoma展开更多
基金We would like to acknowledge the financial support of the Ministry of Science and Technology of China(Grant No.2021YFC2900300)the National Natural Science Foundation of China(Grant Nos.41772074 and 42172103).
文摘Due to the combined influences such as ore-forming temperature,fluid and metal sources,sphalerite tends to incorporate diverse contents of trace elements during the formation of different types of Lead-zinc(Pb-Zn)deposits.Therefore,trace elements in sphalerite have long been utilized to distinguish Pb-Zn deposit types.However,previous discriminant diagrams usually contain two or three dimensions,which are limited to revealing the complicated interrelations between trace elements of sphalerite and the types of Pb-Zn deposits.In this study,we aim to prove that the sphalerite trace elements can be used to classify the Pb-Zn deposit types and extract key factors from sphalerite trace elements that can dis-criminate Pb-Zn deposit types using machine learning algorithms.A dataset of nearly 3600 sphalerite spot analyses from 95 Pb-Zn deposits worldwide determined by LA-ICP-MS was compiled from peer-reviewed publications,containing 12 elements(Mn,Fe,Co,Cu,Ga,Ge,Ag,Cd,In,Sn,Sb,and Pb)from 5 types,including Sedimentary Exhalative(SEDEX),Mississippi Valley Type(MVT),Volcanic Massive Sulfide(VMS),skarn,and epithermal deposits.Random Forests(RF)is applied to the data processing and the results show that trace elements of sphalerite can successfully discriminate different types of Pb-Zn deposits except for VMS deposits,most of which are falsely distinguished as skarn and epithermal types.To further discriminate VMS deposits,future studies could focus on enlarging the capacity of VMS deposits in datasets and applying other geological factors along with sphalerite trace elements when con-structing the classification model.RF’s feature importance and permutation feature importance were adopted to evaluate the element significance for classification.Besides,a visualized tool,t-distributed stochastic neighbor embedding(t-SNE),was used to verify the results of both classification and evalua-tion.The results presented here show that Mn,Co,and Ge display significant impacts on classification of Pb-Zn deposits and In,Ga,Sn,Cd,and Fe
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Most known mineral deposits were discovered by accident using expensive,time-consuming,and knowledgebased methods such as stream sediment geochemical data,diamond drilling,reconnaissance geochemical and geophysical surveys,and/or remote sensing.Recent years have seen a decrease in the number of newly discovered mineral deposits and a rise in demand for critical raw materials,prompting exploration geologists to seek more efficient and inventive ways for processing various data types at different phases of mineral exploration.Remote sensing is one of the most sought-after tools for early-phase mineral prospecting because of its broad coverage and low cost.Remote sensing images from satellites are publicly available and can be utilised for lithological mapping and mineral exploitation.In this study,we extend an artificial intelligence-based,unsupervised anomaly detection method to identify iron deposit occurrence using Landsat-8 Operational Land Imager(OLI)satellite imagery and machine learning.The novelty in our method includes:(1)knowledge-guided and unsupervised anomaly detection that does not assume any specific anomaly signatures;(2)detection of anomalies occurs only in the variable domain;and(3)a choice of a range of machine learning algorithms to balance between explain-ability and performance.Our new unsupervised method detects anomalies through three successive stages,namely(a)stage Ⅰ–acquisition of satellite imagery,data processing and selection of bands,(b)stage Ⅱ–predictive modelling and anomaly detection,and(c)stage Ⅲ–construction of anomaly maps and analysis.In this study,the new method was tested over the Assen iron deposit in the Transvaal Supergroup(South Africa).It detected both the known areas of the Assen iron deposit and additional deposit occurrence features around the Assen iron mine that were not known.To summarise the anomalies in the area,principal component analysis was used on the reconstruction errors across all modelled bands.Our method enhanced the Assen deposit as an anoma
