Abstract: A seismic quantitative prediction method for shale total organic carbon (TOC) based on sensitive parameter volumes is as follows. A target stratum for a TOC content to be measured is determined, logging curves with high correlations with TOC contents are analyzed, the logging curves are found as sensitive parameters; sample data are constructed using the sensitive parameters; a radial basis function (RBF) neural network is trained with the sample data as an input and the TOC content at a depth corresponding to the sample data as an output to obtain a RBF neural network prediction model; sensitive parameter volumes are obtained by using the sensitive parameters and post stack three-dimension seismic data to invert; prediction samples are constructed using the sensitive parameter volumes; the predicted samples are input to the RBF neural network prediction model to calculate corresponding TOC values, thereby the TOC content of the target stratum is predicted.