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In the past decade, deep learning algorithms gained a remarkable interest inthe signal processing community. The availability of big datasets and advancedcomputational resources resulted in developing efficient algorithms. However,such algorithms are biased towards the training dataset. Thus, thetransferability of deep-learning-based operators are challenging, especiallywhen the goal is to apply the learned operator on a new dataset/domain. Lack oftransferability of learned operator across domains hinders the applicability ofdeep learning algorithms in processing seismic data. Unlike camera images, thecomprehensively labeled seismic datasets are not available. Moreover, from onetask to another, the training parameters should be tuned. To remedy thisshortcoming, we have developed a workflow that transfers the learned operatorfrom the camera images to the seismic domain, without modifying its trainingparameters. The similarities in the algorithms and optimization methods incamera and seismic data processing allow us to do so. Accordingly, byincorporating feed-forward de-noising convolutional neural networks (DnCNN) inregularization by de-noising regularizer, we formulate two transferableoptimization problems for de-noising and compressive sensing recovery ofseismic data. Simulated and real-world data examples show the efficiency of ourproposed workflow.

Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data