This thesis suggests the use of a new method of seismic data clustering that can aid in the visualization of seismic maps. Seismic data are primarily made of signal and noise and, due to its dual composition, have asymmetric distributions. Seismic data are traditionally classified by methods that lead the proposed groups` references to their mean values. The mean value is, however, sensitive to noise and outliers and the classification methods that make use of this estimator are, consequently, subjected to generating distorted results. Although other works have suggested the use of the median in cases where the distributions are asymmetric - due to the fact that the estimator is robust with respect to noise and outliers - none have proposed a method that would lead the groups` references to the median while treating seismic data. The method proposed in this work includes, therefore, an algorithm that leads the groups` references to their medians. The iterative treatment of seismic data through the use of a non-linear function that is adequate for the gradient descent generates results with meansquare errors inferior to those of results generated by the use of the mean value. The algorithm`s non- linearity constant determines how the seismic data are led from the mean value towards the median. The proposed method requires little iteration for the results to converge. The proposed method can, therefore, be used as a tool in the sizing of petroleum reservoirs and can also be used to determine the differences between similar geological structures.