<p>Reverse
time migration (RTM) is a technique used to obtain high-resolution images of
underground reflectors; however, this method is computationally intensive when
dealing with large amounts of seismic data. Multi-source RTM can significantly
reduce the computational cost by processing multiple shots simultaneously.
However, multi-source-based methods frequently result in crosstalk artifacts in
the migrated images, causing serious interference in the imaging signals.
Plane-wave migration, as a mainstream multi-source method, can yield migrated
images with plane waves in different angles by implementing phase encoding of
the source and receiver wavefields; however, this method frequently requires a
trade-off between computational efficiency and imaging quality. We propose a
method based on deep learning for removing crosstalk artifacts and enhancing
the image quality of plane-wave migration images. We designed a convolutional
neural network that accepts an input of seven plane-wave images at different
angles and outputs a clear and enhanced image. We built 505 1024×256 velocity
models, and employed each of them using plane-wave migration to produce raw images at 0°, ±20°, ±40°, and ±60° as input of the
network. Labels are high-resolution images computed from the corresponding
reflectivity models by convolving with a Ricker wavelet. Random sub-images with
a size of 512×128 were used for training the network. Numerical examples
demonstrated the effectiveness of the trained network in crosstalk removal and
imaging enhancement. The proposed method is superior to both the conventional
RTM and plane-wave RTM (PWRTM) in imaging resolution. Moreover, the proposed
method requires only seven migrations, significantly improving the
computational efficiency. In the numerical examples, the processing time
required by our method was approximately 1.6% and 10% of that required by RTM
and PWRTM, respectively.</p>
Deep learning for enhancing multi-source reverse time migration