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An important step of seismic data processing is removing noise, includinginterference due to simultaneous and blended sources, from the recorded data.Traditional methods are time-consuming to apply as they often require manualchoosing of parameters to obtain good results. We use deep learning, with aU-net model incorporating a ResNet architecture pretrained on ImageNet andfurther trained on synthetic seismic data, to perform this task. The method isapplied to common offset gathers, with adjacent offset gathers of the gatherbeing denoised provided as additional input channels. Here we show that thisapproach leads to a method that removes noise from several datasets recorded indifferent parts of the world with moderate success. We find that providingthree adjacent offset gathers on either side of the gather being denoised ismost effective. As this method does not require parameters to be chosen, it ismore automated than traditional methods.

Seismic data denoising and deblending using deep learning