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Metallogenic Regularity And Metallogenic Area Of Pb-Zn Deposit In Leibo County,Sichuan Liangshan Ding Jiaping And Shang Tianba Region

Posted on:2019-05-22Degree:MasterType:Thesis
Country:ChinaCandidate:X S XuFull Text:PDF
GTID:2480306305459234Subject:Geology
Abstract/Summary:PDF Full Text Request
In this paper,the lead and zinc deposits in Ding Jia Ping and Shang Tianba region,Leibo County,Liangshan,Sichuan,are studied.Based on the principles and rules of basic geology and tectonic geology,the tectonic evolution,metallogenic background and favorable conditions of the study area are determined,and the typical ore deposits are analyzed and the lead-zinc deposits in the study area are analyzed.The metallogenic regularity is discussed.Based on the GIS and MATLAB platform,the metallogenic characteristics and metallogenic prediction of lead-zinc deposits in the study area are carried out based on the method and technology of mineral resources exploration,using the method and technology of mineral resources exploration,based on the favorable conditions for the mineralization of lead and zinc deposits in the study area.The quantitative analysis of mineralization related factors(aeromagnetic Delta T anomalies,residual gravity anomalies,fracture structures,strata and geochemical anomalies)of the lead and zinc deposits(points)in the Ding Jia Ping and Shang Tianba region,Leibo County,based on the GIS evidence weighting method,indicates that the lead and zinc deposits in the study area are controlled by secondary faults and strata,and Sb,As and C D and Hg have better indications for Pb and Zn.According to the weight value of the posterior probability of the evidence weight method,the favorable location of the metallogenic area in the study area is well divided.It is believed that the favorable areas of the mineralization are mainly distributed in Yang Tianwo,Xi Sujiao,Long Toushan,Si Ziping,Qing Longju and Hui Longba.The BP neural network method is used to train all the selected units with the number of more than three geological factors,and then the data are extracted and predicted based on the samples of the aeromagnetic Delta T value,the residual gravity anomaly and the fracture structure.Finally,based on the correlation coefficient,the prospective area is divided into the geological factors in the key areas.Within the range of the ideal value of the fitting correlation coefficient,the prediction effect is better,and the favorable ore-forming areas of the five regions of Long Toushan,Yang Tianwo,Qing Longju,Xi Ziping and Xi Sujiao are emphatically delineated with the fitting curve.
Keywords/Search Tags:Leibo County, Lead-zinc deposit(point), comprehensive geological factor, weight method of evidence, BP neural network
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