用于大规模三维地震层位跟踪的全可逆神经网络
Fully reversible neural networks for large-scale 3D seismic horizon tracking
摘要：
跟踪地震图像或3D体积中的层位是震波揭示的组成部分。在过去的几十年里，我们看到了使用神经网络来完成这项任务的进展，从用于1D跟踪的浅层网络开始，到用于大型2D图像的更深层卷积神经网络。由于地质结构本质上是三维的，我们希望通过在三维地震资料立方体上训练网络来改进层位跟踪。虽然存在用于各种震波揭示任务的一些3D卷积神经网络，但是由于存储器限制，它们限于浅层网络或相对较小的3D输入。网络状态和权重所需的内存随着网络深度的增加而增加。我们提出了一个完全可逆的网络的地平线跟踪，有一个内存的要求，是独立的网络深度。为了解决网络权重的内存问题，我们使用直接以因子分解形式进行训练的层。因此，我们可以保持大量的网络通道，同时保持卷积核的数量较低。我们使用节省的内存将数据的输入大小增加一个数量级，这样网络就可以更好地从数据中的大型结构中学习。一个实际数据实例验证了所提出的网络结构适用于地震层位跟踪。
英文摘要：
Tracking a horizon in seismic images or 3D volumes is an integral part of seismic interpretation. The last few decades saw progress in using neural networks for this task, starting from shallow networks for 1D traces, to deeper convolutional neural networks for large 2D images. Because geological structures are intrinsically 3D, we hope to see improved horizon tracking by training networks on 3D seismic data cubes. While there are some 3D convolutional neural networks for various seismic interpretation tasks, they are restricted to shallow networks or relatively small 3D inputs because of memory limitations. The required memory for the network states and weights increases with network depth. We present a fully reversible network for horizon tracking that has a memory requirement that is independent of network depth. To tackle memory issues regarding the network weights, we use layers that train in a factorized form directly. Therefore, we can maintain a large number of network channels while keeping the number of convolutional kernels low. We use the saved memory to increase the input size of the data by order of magnitude such that the network can better learn from large structures in the data. A field data example verifies the proposed network structure is suitable for seismic horizon tracking.
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