常规的网格层析反演是基于反射波的走时与共成像点的剩余曲率来构建矢量方程从而更新速度, 然而, 在复杂地表区域的单炮信噪比低, 反射信息不足等因素导致近地表区域速度建模精度不足, 且缺少层位与断层的约束, 无法准确还原深层的实际构造。目前工业中广泛使用的方法是将初至波层析反演得到的浅层速度以向下平移一定深度的地表高程面作为融合面与反射波层析得到的中深层速度相融合, 但这种方法会在融合区出现明显的融合痕迹, 且融合处的速度值会发生突变。为此, 本文从层析反演的原理出发, 根据初至波射线密度与两模型差值来拾取融合面, 并构建符合工区地质背景的融合函数进行速度融合, 最大限度消除融合痕迹与融合处速度值突变的问题, 并在之后的层析反演过程中加入断层约束。本方法运用速度融合, 断控约束以及改善剩余曲率拾取方法来提高复杂山地区的速度建模与成像精度。复杂模型结果证明了正确性, 实际资料测试结果证明了适用性。

Conventional grid-tomographic inversion is based on the travel time of the reflected waves and the residual curvature of the co-imaged points to construct vector equations and thus update the velocities, however, in the complex surface region, factors such as low signal-to-noise ratio of a single shot and insufficient reflection information lead to insufficient accuracy of velocity modelling in the near-surface region, and the lack of layers and fault constraints, which can not accurately restore the actual structure of the deeper layers. The current method widely used in industry is to fuse the shallow velocities obtained from the tomographic inversion by first breaks with the surface elevation plane at a certain depth downward as the fusion surface and the mid-deep velocities obtained from the grid tomography, but this method will show obvious fusion marks in the fusion area and the velocity values at the fusion point will change abruptly. For this reason, this paper starts from the principle of tomography inversion by first breaks, picks up the fusion surface based on the first-to-wave ray density and the difference between the two models, and constructs a fusion function for velocity fusion that fits the geological background of the work area, so as to eliminate the problem of fusion trace and abrupt changes in velocity values at the fusion trace to the maximum extent, and add fault constraints during the subsequent stratigraphic inversion. This method uses velocity fusion, fault-control constraints and improved residual curvature pickup method to improve velocity modelling and imaging accuracy in complex mountain regions. The complex model results prove the correctness, and practical data test results prove the applicability.