论文标题
3D地震反转的编码器架构
Encoder-Decoder Architecture for 3D Seismic Inversion
论文作者
论文摘要
颠倒地震数据以建立3D地质结构是一项艰巨的任务，这是由于大量获得的地震数据，以及由于波动方程的迭代数值解决方案而引起的最高计算负载，这是行业标准工具（例如Full Pave Form Formion（FWI））所要求的。例如，在一个4.5公里$ \ $ 4.5公里的地面尺寸的区域中，3D模型重建需要数百个地震射击场立方体，从而导致记录的数据的Terabytes。本文提出了一种深度学习解决方案，用于在地震调查中记录的场噪声的存在下重建现实的3D模型。我们实施和分析了一个卷积编码器架构，该体系结构有效地处理了数百种地震射击场立方体的整个集合。提出的解决方案表明，在存在10db信号 - 噪声比率下的场噪声的情况下，可以以结构相似性指数度量（SSIM）为0.8554（在1.0中）重建现实的3D模型。
Inverting seismic data to build 3D geological structures is a challenging task due to the overwhelming amount of acquired seismic data, and the very-high computational load due to iterative numerical solutions of the wave equation, as required by industry-standard tools such as Full Waveform Inversion (FWI). For example, in an area with surface dimensions of 4.5km $\times$ 4.5km, hundreds of seismic shot-gather cubes are required for 3D model reconstruction, leading to Terabytes of recorded data. This paper presents a deep learning solution for the reconstruction of realistic 3D models in the presence of field noise recorded in seismic surveys. We implement and analyze a convolutional encoder-decoder architecture that efficiently processes the entire collection of hundreds of seismic shot-gather cubes. The proposed solution demonstrates that realistic 3D models can be reconstructed with a structural similarity index measure (SSIM) of 0.8554 (out of 1.0) in the presence of field noise at 10dB signal-to-noise ratio.
