增强地震成像：一种创新的深度学习方法
Enhancement of seismic imaging: An innovative deep learning approach
摘要：
提高地震资料的频带宽度一直是地球物理学界的追求。高分辨率的地震资料是获取详细地层信息的关键资源。在这里，介绍了一种基于深度学习模型的新方法，通过从测井数据中提取反射，通过提高低频和高频来拓宽地震数据的频谱带宽。从提高地震资料的分辨率和消除地震子波的旁瓣伪影两个方面观察到了相应的改善。在深度学习模型的训练阶段，通过同时考虑多口井，充分保证了地理空间信息，这确保了在反演过程中横向和纵向地质信息受到井控的约束并精确远离井控。大量的实验证明，增强的地震数据与测井信息是一致的，并尊重从给定位置的井定义的岩石性质关系。还可以对不确定性分析进行定量评估，以利用拟议方法的产出来确定一系列地震反应的可能性。
英文摘要：
Enhancing the frequency bandwidth of the seismic data is always the pursuance at the geophysical community. High resolution of seismic data provides the key resource to extract detailed stratigraphic knowledge. Here, a novel approach, based on deep learning model, is introduced by extracting reflections from well log data to broaden spectrum bandwidth of seismic data through boosting low and high frequencies. The corresponding improvement is observed from the enhancement of resolution of seismic data as well as elimination of sidelobe artifacts from seismic wavelets. During the training stage of deep learning model, geo-spatial information by taking consideration of multiple wells simultaneously is fully guaranteed, which assures that laterally and vertically geological information are constrained by and accurate away from the well controls during the inversion procedure. Extensive experiments prove that the enhanced seismic data is consistent with well log information, and honors rock property relationships defined from the wells at given locations. Uncertainty analysis could also be quantitatively assessed to determine the possibilities of a range of seismic responses by leveraging the outputs from the proposed approach.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
