  Seismic images often contain both coherent and random artifacts which
complicate their interpretation. To mitigate these artifacts, we introduce a
novel unsupervised deep-learning method based on Deep Image Prior (DIP) which
uses convolutional neural networks. Our approach optimizes the network weights
to refine the migration velocity model, rather than the seismic image,
effectively isolating meaningful image features from noise and artifacts. We
apply this method to synthetic and real seismic data, demonstrating significant
improvements over standard DIP techniques with minimal computational overhead.
