Preliminary Study On Metallogenic Prospectivebased On Multiple Remote Sensing Information

As one of the most important and hopeful mineral resource potential metallogenicbelt in western China, the Tibetan Gangdese metallogenic belt has a great prospectingpotential in focus. While latelythe Country and the Tibet autonomous all put forwardseries of policies, requires that prospecting and development advantage of mineralresources together and orderly, to build Tibet into a national important base forexploitation of mineral resources and important strategic resource reserves.TheTibetan Gangdese metallogenic beltforms the complex geomorphic types andgeological tectonic framework between the interactionof climate and geologicalstructure.It’soriginal landform types can be roughly divided into three parts assouthern mountain lake basin Hu-Pen-Ou valley region, the northern plateau andalpine valleys area in the east. On tectonic framework the role is located between theclass-nujiang fault and the yarlung zangbo river fault zone. These two north andsouth sides of the faults with large fracture zone goes nearly east-west, shown by thethrust nappe movement characteristics of the north to south.Using spatial analysis and statistical techniques, this paper summarizes researcharea’s geography and geology features, like the topographical features, basin, strataand lithology, rock mass, structure relations, the remote sensing alteration anomaliesinformation. Extracted and quantification, correlation analysis with these features,finding out ore-forming multivariate data related to mineralization anomaly area.Using comprehensive analysis in metallogenic relationship, computing multivariateregression model, then identify metallogenic prospect areas. This has carried on thebeneficial attempt for the next step work in the study area. This article’s mainresearch content is as follows: (1) Study the bottom with the geographical and geological data, extractgeography features from DEM data, and remote sensing information, constructioninformation, geological and known ore deposits (or points) and vectorquantizationthem. Statistical analysis the characteristics of the study area like thegeographical conditions, regional geological setting, ore-controlling tectonic outputform, relationship of the line-annular structure, rock and ore formation distribution,remote sensing alteration anomalies distribution and, etc.(2) Extract metallogenic characteristics of the study area as geologicalgeographical characteristics factors. By lots of experiments and analysis ofmetallogenic characteristics factors, extracted six geological characterization factorsaccording to the geological complexity and entropy method of the calculationprinciple and method: stratigraphic complexity, structural complexity, structure suchas density, node density, the complexity of rock mass, the remote sensing alterationanomaly combination entropy; extracted three factors according to geographycharacterization factor calculation principles and methods: drainage density, degree ofrelief, surface roughness.(3) Refining characteristics of the factors quantity and quality in the study area.By means of covariance, correlation theory and spatial analysis platform, analysis ofthe nine characteristics’ relationship among factors, extract highly relevant features’principal component, remove the independent composition of ore deposits (orpoints)，structure complexity and structural similarity were high density, usingprincipal component analysis (PCA) to extract the constituent of related tomineralization. Recalculate and extract the pixels values of the standardized eightfactors where ore deposits (or points) distribution.(4) Comprehensive mineralization anomaly characteristic of the factors formultivariate data regression analysis, and established multivariate regression modelsin the study area is. Set up a multiple linear regression model, by computingcomprehensive analysis of statistical correlation, with the previous step of refining tenmineralization anomaly characterization factors and ore deposits (points).Usingregression models to calculate the mineralizationvision, select four degreesofmetallogenic prospective areas.