The seismic wavelet that propagates through the subsurface contains a broadband frequencies range. Spectral decomposition technique can breaks down and decomposes the recorded geological events into a series of frequency slices that can be used for geological interpretation. However, it is laborious and time-consuming to analyze and to interpret each seismic frequency section. In this context, we propose a multivariate technique based on independent component analysis (ICA) with the goal of choosing spectral components that best represents the whole seismic spectrum while keeping the main geological information. The independence between two components is a more robust statistical concept than the non-correlation and, in principle, allows the extraction of more significant information from the data. Because ICA does not rank the data by their variances as in principal component analysis (PCA), we have adopted a strategy based on the spectral eingenvalues to order the data previously to the application of ICA. To illustrate the proposed method, we have used both synthetic and real seismic data from an offshore brazilian basin. In both cases, by representing the three best independent components in RGB color space, it was possible to better identify the main geological features.


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