This paper described a method for reconstruction of detailed-resolution depth structure maps, usually obtained after the 3D seismic surveys, using the data from 2D seismic depth maps. The method uses two algorithms based on the generative-adversarial neural network architecture. The first algorithm StyleGAN2-ADA accumulates in the hidden space of the neural network the semantic images of mountainous terrain forms first, and then with help of transfer learning, in the ideal case - the structure geometry of stratigraphic horizons. The second algorithm, the Pixel2Style2Pixel encoder, using the semantic level of generalization of the first algorithm, learns to reconstruct the original high-resolution images from their degraded copies (super-resolution technology). There was demonstrated a methodological approach to transferring knowledge on the structural forms of stratigraphic horizon boundaries from the well-studied areas to the underexplored ones. Using the multimodal synthesis of Pixel2Style2Pixel encoder, it is proposed to create a probabilistic depth space, where each point of the project area is represented by the density of probabilistic depth distribution of equally probable reconstructed geological forms of structural images. Assessment of the reconstruction quality was carried out for two blocks. Using this method, credible detailed depth reconstructions comparable with the quality of 3D seismic maps have been obtained from 2D seismic maps.
翻译:本文介绍了重建详细分辨率深度结构图的方法,通常在3D地震勘测后,使用2D地震深度图的数据,该方法使用两种基于基因对抗神经网络结构的算法。第一个算法SylGAN2-ADA首先在神经网络的隐蔽空间中积累山区地形形态的语义图像,然后在理想情况下帮助转移学习,即地形地平线的结构几何学。第二个算法,即Pixel2Style2Pixel编码器,使用第一种算法的语义化一般化水平,学习从退化的复制品(超分辨率技术)中重建原始的高分辨率图像。展示了将地形地平面边界结构形式的知识从深处从深处传到深处地区。利用Pixel2Style2PixPixder的多式合成,建议创建一种不稳定深度空间,其中项目区域的每个点都从其退化的复制品(超分辨率技术)中重建原始高分辨率图像。用可比较的地质深度图解度图解2的深度进行精确的重建。