地震与大地电磁测深数据的自适应正则化同步联合反演

Adaptive regularized synchronous joint inversion of MT and seismic data

在传统的联合反演研究中,地球物理学者往往更多地关注数据拟合,很少涉及正则化理论.本文在电阻率和速度随机分布的大地电磁测深(MT)与地震联合反演研究的基础之上,将正则化思想引入到同步联合反演中,加入先验信息进行模型约束,选取最小模型为稳定泛函,并首次采用自适应正则化算法来确定联合反演的正则化因子.根据以往研究成果,采用非线性模拟退火方法来实现MT视电阻率或相位与地震走时的同步联合反演.此外,为了验证该算法的有效性,在模型对比试验中设计了4种不同方案.通过模型试验的对比分析,我们认为加入有效模型约束的自适应正则化联合反演,可以有效地提高解的稳定性和计算效率,并能在一定程度上解决不同地球物理数据加权系数人为选取问题,模型试验结果也表明了自适应正则化联合反演优于MT单独反演.

What geophysicists concerned in the previous studies on joint inversion just was data misfit with little consideration about regularization theory. In this paper, based on the joint inversion method with random resistivity and velocity distributions we studied, the concept of regularization is introduced into synchronous joint inversion of magnetotelluric （MT） and seismic data. We set stabilizer as the smallest model involving priori information. We use adaptive method to determine the value of the regularization parameter,and apply it into joint inversion for the first time. Based on our previous studies, nonlinear simulated annealing is adopted to realize apparent resistivity, phase and travel-time joint inversion. Four projects with different methods are designed in the contrast test. Test on synthetic data sets shows that the adaptive regularized joint inversion with effective model constraint has powerful capability to improve the stability of solution and efficiency of computation, and to help us to handle the difficult problem of selection weight scaling factors to a certain extent. The results of model test also reveal that adaptive regularized joint inversion is superior to MT single inversion.