Deep learning and data-driven approaches have shown great potential in scientific domains. The promise of data-driven techniques relies on the availability of a large volume of high-quality training datasets. Due to the high cost of obtaining data through expensive physical experiments, instruments, and simulations, data augmentation techniques for scientific applications have emerged as a new direction for obtaining scientific data recently. However, existing data augmentation techniques originating from computer vision, yield physically unacceptable data samples that are not helpful for the domain problems that we are interested in. In this paper, we develop new physics-informed data augmentation techniques based on convolutional neural networks. Specifically, our generative models leverage different physics knowledge (such as governing equations, observable perception, and physics phenomena) to improve the quality of the synthetic data. To validate the effectiveness of our data augmentation techniques, we apply them to solve a subsurface seismic full-waveform inversion using simulated CO$_2$ leakage data. Our interest is to invert for subsurface velocity models associated with very small CO$_2$ leakage. We validate the performance of our methods using comprehensive numerical tests. Via comparison and analysis, we show that data-driven seismic imaging can be significantly enhanced by using our physics-informed data augmentation techniques. Particularly, the imaging quality has been improved by 15% in test scenarios of general-sized leakage and 17% in small-sized leakage when using an augmented training set obtained with our techniques.
翻译:深层学习和数据驱动方法在科学领域显示出巨大的潜力。数据驱动技术的前景取决于大量高质量培训数据集的可用性。由于通过昂贵的物理实验、仪器和模拟获取数据的成本高昂,科学应用的数据增强技术最近成为获取科学数据的新方向。然而,现有来自计算机视觉的数据增强技术产生了无法帮助我们感兴趣的领域问题的物理上无法接受的数据样本。在本文件中,我们开发了基于革命神经网络的新的物理知情数据增强技术。具体地说,我们的基因模型利用了不同的物理知识(例如管理方程式、观察感知和物理现象)来提高合成数据的质量。为了验证数据增强技术的有效性,我们应用了这些数据增强技术,利用模拟的CO$2美元渗漏数据解决地下地震全波转换问题。我们感兴趣的是,与非常小的CO$2美元渗漏相关的次表层速度模型。我们利用全面的数字测试来验证我们的方法的性能。 Via 对比和分析显示,在使用更精确的模型中,我们用15度的精确度的测算法,通过升级的测算法,通过增强的精确的测算法改进了15度的测深度的测算方法,可以大大地改进了15号的测算。