论文标题
MDA GAN：基于对抗性学习的3-D地震数据插值和复杂缺失的重建
MDA GAN: Adversarial-Learning-based 3-D Seismic Data Interpolation and Reconstruction for Complex Missing
论文作者
论文摘要
缺失痕迹的插值和重建是地震数据处理中的关键步骤，此外，这也是一个高度不良的问题，尤其是对于复杂的病例，例如高比率随机离散丢失，连续且缺失和缺失的富含断层或盐体的调查。这些复杂的案例在当前作品中很少提及。为了应对复杂的丢失病例，我们提出了一种新型的3-D GAN框架的多维对抗gan（MDA GAN）。它可以在3D复合物使用三个歧视器丢失重建后保持数据的各向异性和空间连续性。该功能缝合模块的设计和嵌入在发电机中，以保留输入数据的更多信息。 TANH横熵（TCE）损失是得出的，该损失为生成器提供了最佳的重建梯度，以使生成的数据更加顺畅且连续。我们通过实验验证了研究的各个组件的有效性，然后在多个可公开的数据上测试了该方法。该方法实现了多达95％的随机离散缺失和100个连续缺失的痕迹的合理重建。在断层和盐体丰富的调查中，MDA GAN对于复杂情况仍然可以产生有希望的结果。在实验上，已经证明，在简单和复杂的情况下，我们的方法的性能要比其他方法更好。https：//github.com/douyimin/mda_gan
The interpolation and reconstruction of missing traces is a crucial step in seismic data processing, moreover it is also a highly ill-posed problem, especially for complex cases such as high-ratio random discrete missing, continuous missing and missing in fault-rich or salt body surveys. These complex cases are rarely mentioned in current works. To cope with complex missing cases, we propose Multi-Dimensional Adversarial GAN (MDA GAN), a novel 3-D GAN framework. It keeps anisotropy and spatial continuity of the data after 3D complex missing reconstruction using three discriminators. The feature stitching module is designed and embedded in the generator to retain more information of the input data. The Tanh cross entropy (TCE) loss is derived, which provides the generator with the optimal reconstruction gradient to make the generated data smoother and continuous. We experimentally verified the effectiveness of the individual components of the study and then tested the method on multiple publicly available data. The method achieves reasonable reconstructions for up to 95% of random discrete missing and 100 traces of continuous missing. In fault and salt body enriched surveys, MDA GAN still yields promising results for complex cases. Experimentally it has been demonstrated that our method achieves better performance than other methods in both simple and complex cases.https://github.com/douyimin/MDA_GAN
