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Seismic coherent noise is often found in post-stack seismic data, whichcontaminates the resolution and integrity of seismic images. It is difficult toremove the coherent noise since the features of coherent noise, e.g.,frequency, is highly related to signals. Recently, deep learning has proven tobe uniquely advantageous in image denoise problems. To enhance the quality ofthe post-stack seismic image, in this letter, we propose a noveldeep-residual-learning-based neural network named DR-Unet to efficiently learnthe feature of seismic coherent noise. It includes an encoder branch and adecoder branch. Moreover, in order to collect enough training data, we proposea workflow that adds real seismic noise into synthetic seismic data toconstruct the training data. Experiments show that the proposed method canachieve good denoising results in both synthetic and field seismic data, evenbetter than the traditional method.

Seismic Coherent Noise Removal with Residual Network and Synthetic Seismic Simples