Study On The Prediction Of Remote Sensing Iron Anomaly Information In Different Deposit Types

The study area is located in the eastern section of the Gangdis mineralization belt in the Tibet Autonomous Region,in south-central Tibet,the middle reaches of the Yarlung Tsangpo River and the eastern section of the Gangdis Mountain Range,adjacent to the valley of the Yarlung River and the alpine landscape with deep erosion cuts.The terrain is generally high from north to south and low in the middle,high in the northeast and low in the southwest,with cold climate,backward economy,poor transportation conditions,difficult field work conditions and tough conventional mineral exploration work.Remote sensing has the advantage that conventional mineral exploration cannot match.Remote sensing images have a wide field of view and can reflect the relationship between various geological phenomena in a macroscopic way.Previously,the mineralized alteration of individual deposits in the eastern section of the Gondis metallogenic belt has been studied in detail and depth,but no systematic study has been done on the iron anomaly information in the eastern section of the Gondis.In the present study,a systematic study of iron anomalies in the eastern section of the Gondwana mineralized zone was conducted by examining typical deposits in the porphyry,siliciclastic and porphyry-silica deposits in the Gondwana mineralized zone.In this study,we propose to use Landsat8 remote sensing data and ASTER remote sensing data to extract iron-stained alteration information from porphyry deposits in the eastern section of the Gondis metallogenic belt,such as Xiongcun and Kelong deposits in porphyry deposits,Letingang,Leqingla and Galap deposits in silica deposits,and Kema deposits in porphyry-silica deposits,using principal component analysis and independent component analysis,respectively.For Landsat8 remote sensing data,four bands of OLI2,OLI4,OLI5 and OLI6 were used for extraction,and for ASTER remote sensing data,four bands of ASTER 1,ASTER 2,ASTER 3 and ASTER 4 were used for extraction,and the feature vector tables of principal component analysis and independent component analysis were obtained The extraction maps of iron-stained alteration in each study area were also obtained.The extraction method and extraction accuracy are evaluated next.The extraction method is evaluated by quantifying the percentage of alteration anomalies in each zone;the extraction accuracy is evaluated by confusion matrix.After field field observation,the extracted iron-staining alteration information has a good match with the actual mine site.The PCA method is one of the well known methods,while the ICA method has only been utilized in recent years.the PCA method and the ICA method have their own advantages in extracting different deposits.the PCA method covers more anomalous areas,and the ICA method can accurately extract areas of strong alteration.the PCA method is a dimensionality reduction method that can amplify useful alteration information and weaken the interference of irrelevant background factors.the ICA method is based on high-order statistics The PCA method and the ICA method have different focuses,but both can extract the alteration information well.By conducting comparative evaluation,the applicability of the two methods to different deposits was derived separately.The alteration information extracted by both the PCA method and the ICA method are better for the Xiongcun and Duelong deposits of porphyry type.Among the three ore zones of Letingang,Leqingla and Garap in the silica-type deposits,Letingang and Garap are Fe-polymetallic ores,which are more suitable for Fe-staining alteration extraction by PCA method;Leqingla is an Fe-Pb-Zn ore,which is more suitable for Fe-staining alteration information using ICA method.The alteration information extracted by both PCA method and ICA method is better for Kema ore in porphyry-silica type deposits.In the extraction of mineralized alteration,the combination of PCA and ICA methods can improve the extraction accuracy of remote sensing alteration information by complementing each other.