Год издания: 2022

We present a novel workflow for the accelerated signal enhancement of massive 3D prestack seismic data utilizing a Local Wavefront Attributes Deep Neural Network. It is based on automatic local wavefront attributes estimation using a specially trained convolutional deep neural network. The general workflow is adaptive to a particular 3D prestack seismic volume. It requires performing a conventional semblance-based estimation of wavefront dips and curvatures for only about 1% of the whole amount of data. The verification of the proposed approach is done on challenging real datasets, both marine and land. Deep learning allows achieving a significant speed-up compared to the conventional method while preserving an acceptable quality of the results.