A properly designed skip-connection convolutional autoencoder deep generator is able to capture the inner structure of shot gathers from subsampled seismic data without any pre-training procedure. The complete interpolated data can be reconstructed by feeding the autoencoder with multidimensional random noise and minimizing the mean squared error between generated and measured data. The performances achieved on synthetic and field data show the effectiveness of the proposed method.

A Deep Prior Convolutional Autoencoder for Seismic Data Interpolation

Kong, F.;Lipari, V.;Picetti, F.;Bestagini, P.;Tubaro, S.
2020

Abstract

A properly designed skip-connection convolutional autoencoder deep generator is able to capture the inner structure of shot gathers from subsampled seismic data without any pre-training procedure. The complete interpolated data can be reconstructed by feeding the autoencoder with multidimensional random noise and minimizing the mean squared error between generated and measured data. The performances achieved on synthetic and field data show the effectiveness of the proposed method.
EAGE Annual Conference & Exhibition
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11311/1201550
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