GPS、INSAR技术是目前广泛采用的监测地表形变的有效手段。本项目旨在GPS与INSAR数据融合建立地面三维形变场的方法研究。针对两类测量结果在时空尺度和测量精度上的不一致性，研究内容为：1）融合函数模型研究。以GPS观测为强约束，通过构建附加系统误差拟合函数的函数模型消弱INSAR数据中残留系统误差的影响；针对形变区域内存在的局部随机形变设计函数模型和统计模型相结合的随机变形信号的改正算法；以地球物理模型作为参考框架，建立多类型数据融合的贝叶斯估计模型。2）融合随机模型研究。采用随机模型验后估计方法分别研究不同几何观测技术观测结果、不同参考框架观测数据、系统参数和随机参数间权比关系，统一不同类型观测数据对模型参数估计的贡献。3）顾及异常误差、模型误差、系统误差间交叉影响，研究融合模型的自适应算法。本项目研究可望为高精度三维地表形变的获取提供一个新的途径。

Currently, GPS and INSAR are two effective means widely applied to surface deformation monitoring. In this project, the method to establish the surface 3D deformation field base on the integration of GPS and INSAR data will be studied. For two types of measurements are not coincident in space and time scales and measurement accuracy, research is focused on: 1) Establishment of function model. Systematic error function will be added to integration model to weaken the influence of INSAR residual errors. Correction algorithm for random deformation will be designed by integration of function model and statistical Model. A Bayes model integrated by multiple types of data will be established in the reference frame of geophysical model. 2) Establishment of random model. Radom model posteriori estimation will be introduced to balance the contribution to the model parameters between deferent geophysical observation system, between data with deferent reference frame, also between parameters in same model but with deferent characteristic. 3) Adapted algorithm of integration will be researched in order to prevent the cross infection of abnormal error, model error and statistic error. It is expected to develop a new way to obtain a high-precision three-dimensional deformation field.