Abstract:Lithology of 2-D post-stack seismic section can be predicted with the use of BP algorithm of neural network. First,the porosity and shale content of formations around the chosen borehole are taken as prediction target in the network, and the seismic parameters (such as instantaneous frequency and relative wave impedance)of borehole-side trace are used as input samples to solve for node-point weight values of each layer. After the training of borehole-side seismic trace is finished,the determined porosity network and shale content network are extrapolated to compute the porosity and shale content of the corresponding formation of other seismic traces.Trial numerical computation indicates that taking radial basic function as exciting function of a hidden layer brings good sample fitting effect.