NEW ASPECTS OF USING NEURAL NETWORK FORECASTING FOR EAST SIBERIA HYDROCARBONS DEPOSITS
Multivariate complex neural network analysis of two and three-dimensional data using clusterization and modificated teaching algorithm in «GeolEdit» environment is considered. The three-dimensional models of distribution of magnetization and density are gained from the areal gravity-magnetic data, the most effective layers are chosen. The frequency dispersion is spent and the most correlated frequency with known discharges is chosen for a mathematical surface of relief. Not all matrix layers covering the studied square, are suitable for the forecast. Predictive estimations of hydrocarbons localization are received (with probability 0,7-0,8) on several sections of West Siberia.