Application of Artificial Neural Networks, (ANN) in providing of self organization feature Map? (SOFM) for Exploration of mines Stages
|Category||GIS & Remote sensing|
|Location||21th symposium on geosciences|
|Holding Date||24 May 2008|
Application of Artificial Neural Networks, (ANN) has gained considerable favor in 1980. These applications include mining, geosciences, natural resources and related sciences. An application of their usage is subject of remote sensing. Previously interpretations of remotely sensed data usually was done using manual methods which was a time taking and tedious work accompanied by serious errors and inaccuracies. For this reason many workers in the field put forward much effort to obtain the needed data for geologic and geographic ? natural resources maps by usage of the signals received directly from satellites. For the first time, in the second Conference on Artificial Neural Network convinced in 1992 in Finland, the subject of ? self organization feature Map ?(SOFM) was subject of debate and presentations. The remarkable results of this application were also presented for the two case studies carried out in southern France. From then on this subject picked up a good momentum in developed countries which employed the technique for mapping purposes in mining exploration. In present work by usage and aid of (ANN) satellites? signals with noise filtration and deletion were converted into surface image maps of the earth. These maps reveal all kinds of natural and artificial features of the earth?s surface and are usable as exploration tools in reconnaissance and prospecting stages. In this paper first a summary of A. N. N. s structures and architecture is presented, and then the algorithms used in design of such networks are discussed. Finally, the evolution of the Neural Networks in preparation of (SOFM) and the details of their interpretation is investigated and presented in this work.