摘要
In petroleum seismic exploration,dense seismic ray coverage is often guaranteed through dense seismic sources and geophones.Dense ray coverage facilitates the high-resolution 3D velocity structure imaging of near surfaces using surface waves.In this study,the 3D velocity and anisotropy structure of a petroleum exploration area are obtained using the azimuth-dependent dispersion curve inversion(ADDCI)method.Imaging results show that low-velocity zones correspond to a river channel.The fast propagation direction(FPD)of S-waves along this channel is basically consistent with the direction of the channel.The eastern part of the study area has a surface sediment layer with a thickness of less than 20 m,which corresponds to the sand and gravel deposits formed by the river alluvial deposition near the surface.In addition,a relatively thick sedimentary layer is formed on the southern side of the study area.The anisotropy shows that the FPD is positively correlated with the direction of alluvial fl ow and that the magnitude of anisotropy in the deep part is greater than that in the shallow part.Inversion results are basically consistent with the geological data and suggest that the obtained model can truly refl ect the 3D velocity structure and anisotropy of the near-surface area.This study shows that the ADDCI method can maximize the high-energy surface waves in exploration data to obtain near-surface velocity structures,which provide a highly accurate model for near-surface static correction.
石油地震勘探布设的密集人工震源和检波器使得在勘探工区有着密集射线覆盖,为使用面波进行近地表高分辨率3D速度结构成像提供了便利。本文使用基于多方位角的面波频散反演方法获得了某石油勘探工区的三维速度和各向异性结构。速度成像结果显示穿过反演区域的一条河道对应于明显的低速区域,且其低速层厚度要明显大于周围区域。该河道处的最大快波方向与河道的走向基本一致;在反演区域的东侧有一片厚度小于20m的表层沉积物,与地表表层的河流冲积形成的沙石堆积物对应。同时,由于河流方向为由南向北,在反演区域南侧形成了更厚的沉积层。各向异性结果显示最大快波方向与流水冲积的方向呈现正相关,深部的各向异性强度要大于浅部。反演结果与实际地质资料基本吻合,可以真实地反映近地表区域的三维速度结构及各向异性信息,为了解近地表地质情况提供了具体的资料。本研究表明,基于多方位角面波频散反演方法,可以充分利用勘探数据中的高密度的面波射线获得近地表速度结构,为近地表静校正提供更准确的模型。
基金
supported by the National Key Research and Development Program of China(No.2017YFC0601206)
the Science and Technology Innovation(Seedling Project)Cultivation Program of Sichuan Province in 2020(No.2020127)
the National Natural Science Foundation of China(Nos.41674059,41340009)。