Here, we use the Multilayer perceptron neural network for attenuation of the ground roll from 3D raw seismic data recorded in Algerian Sahara. Firstly, the ground roll of the In-lines of the first swath are attenuated using the F-K filter. Then, a Multilayer perceptron neural network model with Hidden Weight Optimization algorithm is trained in a supervised mode using the raw seismic data of these In-lines as an input and the filtered data as an output and the weights of connection are optimized. Data of other swaths are propagated through the neural network machine; the output of the MLP machine is the filtered seismic data from coherent linear noises.

Comparison between the calculated output and the filtered data using the F-K filter of other swaths shows that the neural machine can be used for automatization of seismic data processing and the linear noise filtering using the F-K method.


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