  Seismic coherent noise is often found in post-stack seismic data, which
contaminates the resolution and integrity of seismic images. It is difficult to
remove the coherent noise since the features of coherent noise, e.g.,
frequency, is highly related to signals. Recently, deep learning has proven to
be uniquely advantageous in image denoise problems. To enhance the quality of
the post-stack seismic image, in this letter, we propose a novel
deep-residual-learning-based neural network named DR-Unet to efficiently learn
the feature of seismic coherent noise. It includes an encoder branch and a
decoder branch. Moreover, in order to collect enough training data, we propose
a workflow that adds real seismic noise into synthetic seismic data to
construct the training data. Experiments show that the proposed method can
achieve good denoising results in both synthetic and field seismic data, even
better than the traditional method.
