变无形为有形：时空约束数据增强的数据驱动地震反演
Making Invisible Visible: Data-Driven Seismic Inversion with Spatio-temporally Constrained Data Augmentation
摘要：
深度学习和数据驱动的方法在科学领域显示出巨大的潜力。数据驱动技术的前景依赖于大量高质量训练数据集的可用性。由于通过昂贵的物理实验、仪器和模拟获得数据的成本很高，用于科学应用的数据增强技术近年来已成为获得科学数据的新方向。然而，现有的数据增强技术源于计算机视觉，产生物理上不可接受的数据样本，这是没有帮助的领域问题，我们感兴趣的。在本文中，我们开发了新的数据增强技术的基础上卷积神经网络。具体来说，我们的生成模型利用不同的物理知识（例如控制方程、可观察的感知和物理现象）来提高合成数据的质量。为了验证我们的数据增强技术的有效性，我们应用它们来解决地下地震全波形反演使用模拟CO $_2$泄漏数据。我们的兴趣是反演地下速度模型与非常小的CO $_2$泄漏。我们使用全面的数值测试验证我们的方法的性能。通过比较和分析，我们表明，数据驱动的地震成像可以显着提高使用我们的数据增强技术。特别是，成像质量已提高了15%，在一般大小的泄漏和17%，在小尺寸泄漏的测试场景时，使用我们的技术获得的增强训练集。
英文摘要：
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 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 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.
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