Deep-learning-based local wavefront attributes and their application to 3D prestack data enhancement

Abstract

The work presents a novel workflow to accelerate the estimation of local wavefront attributes (LWAs) from massive 3D prestack seismic data using deep learning (DL) focusing on data enhancement. A standard estimation method based on a semblance-based brute-force optimization provides good results but is time consuming. A modification of the U-net convolutional neural network, commonly used in image processing applications, is proposed to link the seismic data with the wavefront attributes. Color pixel image input for the neural network is generated through a straightforward seismic data regularization based on supergrouping followed by red, green, and blue encoding. The proposed workflow can be adapted to any 3D prestack seismic volume. Conventional semblance-based attributes estimation is required for the training step but only for approximately 1% of the total data. The prediction step is very efficient and reduces the overall run time significantly. The verification of the proposed approach is performed on challenging real land and marine data sets. As a result, DL-based estimation of LWAs accelerates computation up to 200 times compared to the standard method. The attributes from the proposed DL-based approach indicate an acceptable match compared with the brute-force semblance-based optimization results. Conventional and proposed estimation methods result in comparable prestack data enhancement results for more reliable seismic processing in challenging areas.