论文标题
解剖U-NET用于地震应用：深入学习多次删除的深入研究
Dissecting U-net for Seismic Application: An In-Depth Study on Deep Learning Multiple Removal
论文作者
论文摘要
地震处理通常需要抑制收集数据时出现的倍数。为了解决这些人工制品，从业人员通常依靠基于ra的转换算法作为移民后的调节。但是，这种传统方法既耗时又依赖参数，使其相当复杂。在这项工作中，我们提出了一种基于学习的替代方案，可提供竞争成果，同时降低其用法的复杂性，从而使其适用性民主化。尽管仅接受合成学培训，但在推断复杂的现场数据时，我们在推断复杂的现场数据时会观察到我们网络的出色性能。此外，广泛的实验表明，我们的建议可以保留数据的固有特征，避免了不希望的过度光滑的结果，同时消除了倍数。最后，我们对模型进行了深入的分析，在此分析中，我们可以确定具有物理事件的主要超参数的影响。据我们所知，这项研究的开创了神经网络的拆箱，以帮助用户深入了解网络的内部运行。
Seismic processing often requires suppressing multiples that appear when collecting data. To tackle these artifacts, practitioners usually rely on Radon transform-based algorithms as post-migration gather conditioning. However, such traditional approaches are both time-consuming and parameter-dependent, making them fairly complex. In this work, we present a deep learning-based alternative that provides competitive results, while reducing its usage's complexity, and hence democratizing its applicability. We observe an excellent performance of our network when inferring complex field data, despite the fact of being solely trained on synthetics. Furthermore, extensive experiments show that our proposal can preserve the inherent characteristics of the data, avoiding undesired over-smoothed results, while removing the multiples. Finally, we conduct an in-depth analysis of the model, where we pinpoint the effects of the main hyperparameters with physical events. To the best of our knowledge, this study pioneers the unboxing of neural networks for the demultiple process, helping the user to gain insights into the inside running of the network.
