ChannelSeg3D: channel simulation and deep learning for channel interpretation in 3D seismic images

Seismic channel interpretation involves detecting channel structures which often appear as meandering shapes in 3D seismic images. Many conventional methods are proposed for delineating channel structures using different seismic attributes. However, these methods are often sensitive to seismic discontinuities (e.g., noise and faults) that are not related to channels. We propose a convolutional neural network (CNN) method to improve the automatic channel interpretation. The key problem in applying the CNNs method into channel interpretation is the absence of the labeled field seismic images for training the CNNs. To solve this problem, we propose a workflow to automatically generate numerous synthetic training datasets with realistic channel structures. In this workflow, we first randomly simulate various meandering channel models based on geological numerical simulation. We further simulate structural deformation in the form of stratigraphic folding referred to as “folding structures” and combine them with the previously generated channel models to create reflectivity models and the corresponding channel labels. Convolved with a wavelet, the reflectivity models can be transformed into learnable synthetic seismic volumes. By training the designed CNN with synthetic seismic data, we obtain a CNN which learns the characterization of channel structures. Although trained on only synthetic seismic volumes, this CNN shows an outstanding performance on field seismic volumes. This indicates that the synthetic seismic images created in this workflow are realistic enough to train the CNN for channel interpretation in field seismic images.