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.
翻译:将地震数据倒置以建立3D地质结构是一项艰巨的任务,因为获得的地震数据数量巨大,以及波方的迭代数字解决方案(如全波变换(FWI)等行业标准工具要求的波形方程式的迭代数字解决方案)导致计算负荷极高。 例如,在一个地表尺寸为4.5km $4.5km的面积区域,3D模型重建需要数以百计的地震镜头采集立方体,从而导致记录的数据达到Terabyte。本文件为在地震调查中记录实地噪音的情况下重建现实的3D模型提供了一个深层次的学习解决方案。我们实施并分析一个可高效处理全套收集数百个地震射电加热立方体的共振动编码脱coder结构。拟议解决方案表明,在10dB信号到噪音比率存在实地噪音的情况下,可以通过结构相似指数测量0.8554(在1.0之间)来重建现实的3D模型。