Metallic Mineral Resources Assessment and Analysis System Design

This paper presents the aim and the design structure of the metallic mineral resources assessment and analysis system. This system adopts an integrated technique of data warehouse composed of affairs processing layer and analysis application layer. The affairs processing layer includes multiform databases (such as geological database, geophysical database, geochemical database), while the analysis application layer includes data warehouse, online analysis processing and data mining. This paper also presents in detail the data warehouse of the present system and the appropriate spatial analysis methods and models. Finally, this paper presents the prospect of the system.

Research and application of mineral resources assessment by weights of evidence model based on SIG

Geological data are usually of the characteristics of multi-source, large amount and multi-scale. The construction of Spatial Information Grid overcomes the shortages of personal computers when dealing with geological data. The authors introduce the definition, architecture and flow of mineral resources assessment by weights of evidence model based on Spatial Information Grid (SIG). Meanwhile, a case study on the prediction of copper mineral occurrence in the Middle-Lower Yangtze metallogenic belt is given. The results show that mineral resources assessement based on SIG is an effective new method which provides a way of sharing and integrating distributed geospatial information and improves the efficiency greatly.

Gold Resources Potential Assessment in Eastern Kunlun Mountains of China Combining Weights-of-evidence Model with GIS Spatial Analysis Technique

Resources potential assessment is one of the fields in geosciences,which is able to take great advantage of GIS technology as a substitution of traditional working methods.The gold resources potential in the eastern Kunlun Mountains,Qinghai Province,China was assessed by combining weights-of-evidence model with GIS spatial analysis technique.All the data sets used in this paper were derived from an established multi-source geological spatial database,which contains geological,geophysical,geochemical and remote sensing data.Three multi-class variables,i.e.,structural intersection,Indosinian k-feldspar granite and regional fault,were used in proximity analysis to examine their spatial association with known gold deposits.A prospectivity map was produced by weights-of-evidence model based on seven binary evidential maps,all of which had passed a conditional independence test.The study area was divided into three target zones of high potential,moderate potential and low potential areas,among which high potential areas and moderate potential areas accounted for 20% of the total area and contained 32 of the 43 gold deposits.The results show that the gold resources potential assessment in the eastern Kunlun Mountains has a higher precision.

Digital Geosciences and Quantitative Mineral Exploration

The idea of mineral exploration,which is called"exploration philosophy"in the Western countries,is the thoughts,the methodology,technology,goals and organization that guide mineral exploration.The three basic elements of mineral exploration are"what to find","where to find"and"how to find".The concept of mineral exploration is gradually changing with the development of these three elements that provide a powerful driving force to change mineral exploration concepts,methods and technology.Innovation of mineral exploration concepts is the result of continuing exploration and development keeping pace with the times.The combination of"mathematical geology"and"information technology"can be called"digital geology".Digital geology is the data analysis component of geological science.Geological data science is a science that uses the general methodology of data to study geology based on the characteristics of geological data and the needs of geological field work.Digital mineral exploration is the application of digital geology in mineral exploration to reduce ore-finding uncertainty.