论文详情
层间多次波压制是目前困扰油气地震勘探的难题之一。为推动层间多次波压制方法的发展, 对层间多次波预测及自适应相减方法的研究进展进行了调研, 总结了共聚焦点技术、地表数据分离法、逆散射级数法、虚同相轴法、Marchenko自聚焦方法等备受关注的层间多次波预测方法以及自适应相减法的发展进程, 分析了上述方法的优缺点。共聚焦点技术中层相关算法相比边界算法具有更强的适应性, 但以牺牲计算成本为代价; 逆散射级数法虽然具有较高的多次波预测精度, 但是巨大的计算量限制了其推广应用; 地表数据分离法和虚同相轴法虽然具有较高的计算效率, 但是数据分离过于依赖人工操作; Marchenko自聚焦方法目前在实际应用中尚不成熟。自适应相减方法在一次波和多次波非正交情况下的局限性导致了层间多次波压制方法的诞生。在保证层间多次波压制效果的前提下提高计算效率以及实用性, 是当前层间多次波压制方法的重要发展方向。结合目前研究热点, 层间多次波压制方法与波形反演、深度学习等方法技术的结合可能是未来的发展趋势。
Interbed multiples suppression is a thorny problem in seismic exploration. To promote its development, we investigate the evolution of interbed multiples prediction and adaptive subtraction and give an overview of the methods of high concern, which include common focus point, surface data separation, inverse scattering series, virtual event, and Marchenko autofocusing; we also discuss their advantages and disadvantages. For the common-focus-point method, the layer-related algorithm has stronger adaptability than the boundary-related algorithm, but its computational expense is also high. Inverse scattering series method has high accuracy of multiples prediction, but its application is limited by the large amount of computation. Despite their high computational efficiency, surface data separation and virtual event methods rely too much on manual operation to accomplish data separation. Marchenko autofocusing method is unproved in practical use. The limitations of adaptive subtraction in the context of non-orthogonal primary waves and multiples leads to the generation of inversion-based interbed multiples suppression. The focus is how to improve computational efficiency and practicability on the premise of high accuracy. A potential direction is to combine interbed multiples suppression with waveform inversion and deep learning.