角度域剩余深度对剩余速度的敏感性分析

本文针对地震勘探深度域偏移速度建模研究, 利用角度域共成像点道集(ADCIGS)建立了以剩余速度为自变量, 剩余深度为目标函数的关系式, 及目标函数的梯度公式.利用导出的两个公式分别对剩余深度与剩余速度的关系进行了定量分析.通过理论分析和模型试算证明初始速度模型的误差具有方向敏感性, 即正误差较负误差对速度建模迭代收敛更敏感.利用此结论进行深度域速度建模既可以提高计算效率也可以提高建模精度.

Sensitivity analysis of the residual depth about the residual velocity in the angle domain

The velocity modeling in the depth domain is a key and difficult point in seismic data processing. In recent years, with the rapid development of pre-stack depth migration technology, the velocity modeling in the depth domain is becoming more and more important. Especially, the reverse-time migration method is developing very fast, but also hard to embody its advantages if there is no high accuracy of depth domain speed. So the accurate velocity model building is now a focused topic of active research.#br#At present, in the industrial production, the tomographic technique is a most common method that is used in the depth domain velocity modeling. Firstly, it establishes an initial velocity model by using the speed coherent inversion method. Then it modifies the initial velocity model by using the error of the common reflection point gathers. This approach often requires taking the manual interaction, and the calculation precision is good. However, the conventional common reflection point in the offset domain has a maximum defect, namely there are many false appearances because of the multiple paths. Focusing on the tomographic technique in the depth domain velocity modeling, this paper establishes a relation with the residual velocity as the independent variable and the residual depth as the objective function. And a Gradient formula about the objedctive function is suggested by using the angle domain common image gathers to replace the conventional common reflection point gathers.#br# We use this formula to make the quantitative analysis and model test for the relationship between the residual velocity and the residual depth. The theoretical analysis and model test demonstrate that the errors of initial velocity model have directional sensitivity. Based on the conclusions, the velocity model building can either increase the computational efficiency of migration velocity modeling, or improve the accuracy.#br#The conventional velocity modeling process can be improved by the new cognition. Firstly, we make the initial velocity greater than the real speed. This process needs to take some manual intervention, namely this step is realized through manually jugging whether the gather is upward or downward. On this basis, we utilize the angle domain common image gathers to replace the offset domain common reflection point gathers, and then the initial velocity model can be updated by using the new formula. Tests on model data and real data indicate that the new method has great practical values.