Modeling Method Of Regional Gravity Field Based On Measured Gravity Data

The earth’s gravitational field is a physical quantity that has an important influence on the motion of objects on the earth,and the motion change of materials in the earth can be reflected in the dynamic development and change of the gravitational field.Therefore,gravity measurement and gravitational field modeling are important means to study the law of the earth’s material change.Gravity field modeling has extensive and far-reaching influence in geophysics,Marine science,geodesy,geodesy and other fields.It is a key technical means to integrate national strategic data resources to provide important support for economic and national defense construction.Local gravity field modeling is based on local gravity measurement data and modeling methods,such as multiple interpolation methods,least square configuration methods,etc.In the process of modeling,there are often many problems,such as unsuitable modeling methods,large system error,the influence of White Gaussian noise and data loss.In this paper,the local gravity field model construction focuses on how to select the appropriate method and achieve the optimal parameter configuration,and how to improve the accuracy of the local gravity field model of a specific research method by comparing and improving the methods.The main research content is based on the measured airborne gravity data,the accuracy of each method is analyzed,and the modeling accuracy of the corresponding method is improved through feasible algorithm improvement measures,and the local earth gravity field model is constructed.Least-squares Collocation method(LSC),Inverse Distance weighting method(IDW),and Quadric surface fitting(QSF)were used to analyze the accuracy,and the influences of different noise levels on LSC and IDW were studied.After the analysis and comparison,the error compensation method combined with inverse distance weighting method and Quadric surface fitting method was used to improve the modeling accuracy.The main achievements of this research are as follows:(1)After sampling the airborne gravity data set of a certain density in a certain area of northwest China,a relatively evenly distributed experimental gravity data set is obtained.Based on this data set,the effects of the least square configuration method and the inverse distance weighting method under different noise levels are studied.(2)The inverse distance weighting method shows different interpolation precision with the change of the calculation constraint radius,and finally obtains the calculation radius with the highest accuracy.The accuracy of the inverse distance weighting method under the calculation radius is better than that of the quadric surface method under the same data condition.(3)An error compensation method based on the combination of inverse distance weighting method and quadric surface fitting method is proposed by using the above gravity data set.The quadric surface is used to fit the error of inverse distance weighting method,and then the error compensation is realized in the interpolation results of inverse distance weighting method.The inverse distance weighting method and quadric surface method with root mean square error of 7.8mGal and 4.2mGal can be improved to 3.7mGal by compensation method.