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 have adopted a convolutional neural network (CNN) method to improve automatic channel interpretation. The key problem in applying the CNN method into channel interpretation is the absence of labeled field seismic images for training the CNNs. To solve this problem, we adopt a workflow to automatically generate numerous synthetic training data sets with realistic channel structures. In this workflow, we first randomly simulate various meandering channel models based on geologic 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 that learns the characterization of channel structures. Although trained on only synthetic seismic volumes, this CNN shows 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.

Seismic reflection is a proven and effective method commonly used during exploration of deep mineral deposits in Fujian, China. In seismic data processing, rugged depth migration based on wave-equation migration can play a key role in handling surface fluctuations and complex underground structures. Because wave-equation migration in the shot domain cannot output offset-domain common-image gathers in a straightforward way, the use of traditional tools for updating the velocity model and improving image quality can be quite challenging. To overcome this problem, we have used the attribute migration method. This works by sorting the migrated stack results for every single-shot gather into the offset gathers. The value of the offset that corresponds to each image point is obtained from the ratio of the original migration results to the offset-modulated shot-data migration results. A Gaussian function is proposed to map every image point to a certain range of offsets. This helps improve the signal-to-noise ratio, which is especially important in handling low-quality seismic data obtained during mineral exploration. Residual velocity analysis is applied to these gathers to update the velocity model and improve the image quality. The offset-domain common-image gathers are also used directly for real mineral exploration seismic data with rugged depth migration. After several iterations of migration and updating the velocity, our procedure achieves an image quality better than the one obtained with the initial velocity model. The results can help with the interpretation of thrust faults and deep deposit exploration.