论文标题
深红色的跨层可转移性在去噪声和压缩感应恢复地震数据中的可转移性
Across-domains transferability of Deep-RED in de-noising and compressive sensing recovery of seismic data
论文作者
论文摘要
在过去的十年中，深度学习算法对信号处理社区产生了非凡的兴趣。大数据集和高级计算资源的可用性导致开发有效的算法。但是，这种算法偏向培训数据集。因此，基于深度学习的运营商的可转移性具有挑战性，尤其是当目标是在新的数据集/域上应用学习的操作员时。跨领域的学习操作员缺乏可传递性，阻碍了深度学习算法在处理地震数据中的适用性。与摄像机图像不同，没有全面标记的地震数据集。此外，从一个任务到另一个任务，应调整培训参数。为了解决这一缺点，我们开发了一个工作流，将学习的操作员从相机图像转移到地震域，而无需修改其训练参数。相机和地震数据处理中的算法和优化方法中的相似性使我们能够这样做。因此，通过将卷积的卷积神经网络（DNCNN）纳入正规化，我们通过去噪声正常化程序进行了正规化，我们提出了两个可转移的优化问题，以降低地震数据的去噪声和压缩感应恢复。模拟和现实世界的数据示例显示了我们提出的工作流程的效率。
In the past decade, deep learning algorithms gained a remarkable interest in the signal processing community. The availability of big datasets and advanced computational resources resulted in developing efficient algorithms. However, such algorithms are biased towards the training dataset. Thus, the transferability of deep-learning-based operators are challenging, especially when the goal is to apply the learned operator on a new dataset/domain. Lack of transferability of learned operator across domains hinders the applicability of deep learning algorithms in processing seismic data. Unlike camera images, the comprehensively labeled seismic datasets are not available. Moreover, from one task to another, the training parameters should be tuned. To remedy this shortcoming, we have developed a workflow that transfers the learned operator from the camera images to the seismic domain, without modifying its training parameters. The similarities in the algorithms and optimization methods in camera and seismic data processing allow us to do so. Accordingly, by incorporating feed-forward de-noising convolutional neural networks (DnCNN) in regularization by de-noising regularizer, we formulate two transferable optimization problems for de-noising and compressive sensing recovery of seismic data. Simulated and real-world data examples show the efficiency of our proposed workflow.
