应用全张量重力梯度数据优化密度和速度模型

重力梯度全张量数据具有高灵敏度反映探测目标局部微弱变化的特点,可以对单一地震数据在建立速度模型过程中的不确定性,尤其是各向异性速度模型建立过程中的精细结构进行补充修正.本文主要研究重震两类数据与各自所反映的物理模型关系以及密度模型与速度模型之间的相互联系和制约关系,从而建立重力全张量数据与地震速度模型间的信息互补关系和模型参数间的解析表达式.在此基础上,提出多参数加权正反演算法流程,逐层异常分离技术和约束条件下迭代反演模型修正方法,并实现模块编程和验证.实验数据和结果表明,重力全张量数据和地震数据多参数融合能够明显提高地震速度建模精度,减小模型解释的不确定性.

Optimization of density and velocity model utilizing full tensor gravity gradient data

Full tensor gravity gradient (FTG) data have relatively high sensitivity for representing small changing of exploration objects. It can compensate the drawback of velocity model created by seismic data, especially when anisotropic velocity model is used. In this study, gravity and seismic data are dealt with for obtaining the relationship between their causative sources as well as interactive constraints. The attempt can assist in establishing effective relation between the FTG data and the seismic velocity model. Due to the relation, a multiple parameter forward calculation and inversion algorithm and workflow are proposed by combination with effective weighting. An inversion method of layer-by-layer iterative stripping from shallow to deep is created in the study in order to improve density and velocity model. Programming and implementation has been carried out to test the feasibility of the method. Synthetic and field data test show that the high resolution velocity model can be obtained successfully by means of combination of FTG data and seismic presentation, which indicates that ambiguity of model interpretation can be greatly eliminated.