Structure-oriented filtering in Crossplotting and k-means

André Steklain, Francisco Ganacim, Márcio Adames, João Luis Gonçalves, Danian Steinkirch de Oliveira


Several authors have proposed new techniques using multi-attribute analysis and machine learning. Studying the influence of different data treatments on such techniques is essential. We analyze the results by applying two clustering techniques, Crossplotting, and k-means, in filtered data. In particular, we use structure-oriented filtered seismic data before calculating seismic attributes. We use a migrated section of the Buzios field from the Brazilian pre-salt in the Santos Basin. We find that combining filtering and clustering techniques can improve salt identification.


seismic attributes, machine learning, mass transport complex, salt dome detection

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