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.
翻译:地震处理往往需要抑制数据收集过程中出现的多重数据。 处理这些文物时, 实践者通常依靠Radon 变异算法作为移民后收集条件。 但是, 这些传统方法既耗时又依赖参数, 使这些方法相当复杂。 在这项工作中, 我们提出了一个基于深层次学习的替代方法, 提供竞争性的结果, 同时降低其使用的复杂性, 从而使其适用性民主化。 我们观察到我们的网络在推断复杂的实地数据时表现极好, 尽管事实上只受过合成学的培训。 此外, 广泛的实验表明, 我们的提议可以保存数据的内在特征, 避免不理想的过度移动的结果, 同时消除多重结果。 最后, 我们深入分析模型, 以便用物理事件来定位主要超参数的效果。 根据我们的知识, 这项研究开创了为非多功能过程创建神经网络的绝版, 帮助用户了解网络的内部运行情况。