Aligning seismic images is important in many areas of seismic processing such as time-lapse studies, tomography, and registration of compressional and shear-wave images. This problem is especially difficult when the misalignment is large and varies rapidly and when the images are not shifted versions of each other because they are either contaminated by noise or have different phase or frequency content. In addition, the images may be related by multidimensional vector-valued shift functions. We have developed a fast, scalable, and end-to-end trainable convolutional neural network (CNN) for seismic image registration. The concept of optical flow is widely applied to the problem of image registration using variational methods. Recent developments in the field of computer vision have shown that optical flow estimation can be formulated as a supervised machine learning task and can be successfully solved using CNNs. We train our CNN, SeisFlowNet, on images warped with known shifts and corrupted with noise, frequency, and phase perturbations. We evaluate the promising performance of the trained SeisFlowNet with synthetic data sets where the shift function is known and the images are contaminated with noise and other perturbations. The accuracy of the results obtained with SeisFlowNet is favorably compared with two other popular methods for seismic registration: windowed crosscorrelation and dynamic image warping. Further, we highlight the principles adopted to create training data sets and the advantages and disadvantages of the method.

中文翻译：

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使用多尺度卷积神经网络的地震图像配准

对准地震图像在地震处理的许多领域都很重要，例如延时研究，层析成像以及压缩和剪切波图像的配准。当未对准较大且变化迅速时，以及图像彼此之间没有偏移版本时，此问题尤其困难，因为它们要么被噪声污染，要么具有不同的相位或频率含量。另外，图像可以通过多维矢量值移位函数来关联。我们已经开发了一种快速，可扩展且端到端的可训练卷积神经网络（CNN），用于地震图像配准。光流的概念已广泛应用于使用变分方法的图像配准问题。计算机视觉领域的最新发展表明，光流估计可以公式化为有监督的机器学习任务，并且可以使用CNN成功解决。我们对CNN SeisFlowNet进行训练，使其在出现已知偏移且因噪声，频率和相位扰动而损坏的图像上进行训练。我们使用合成数据集评估训练有素的SeisFlowNet的有前途的性能，在合成数据集中，位移函数已知，并且图像被噪声和其他干扰所污染。用SeisFlowNet获得的结果的准确性与其他两种常用的地震记录方法：窗口互相关和动态图像变形相比，具有优势。此外，我们重点介绍了用于创建训练数据集的原理以及该方法的优缺点。