Clustering and Visualization of Seismic Data

@inproceedings{FleckRM2006,
 abstract = { This work 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 their 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, subject 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 this estimator is robust with respect to noise and outliers – none have proposed a method that would lead the groups’ references to the median value 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 mean-square errors inferior to those generated by the use of the mean value. The algorithm’s non-linearity constant determines how seismic data are led from the mean value towards the median. Convergence can be achieved with little iteration in the proposed method. It 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. },
 author = { Fleck, Ernesto M. AND Pedreira, Carlos Eduardo AND De Araujo Santos, Rogério },
 booktitle = { 26th gOcad Meeting },
 month = { "june" },
 publisher = { ASGA },
 title = { Clustering and Visualization of Seismic Data },
 year = { 2006 }
}