Data Processing of Gravity Base Network in Plateau Area: The Case of Qinghai Province, China

Qiong Wu,Lijiang Zhao,Shi Liu,Linshan Zhong,Hongqing Li,Bin Wang,Shengpeng Zhang

doi:10.3390/rs14051142

Abstract

The latest gravity survey of the gravity base network in Qinghai Province, China, was conducted with six Scintrex CG gravimeters and this gravity survey was tied to existed gravity reference stations. In this gravity network with long segments and very rugged topography, the calibration of scale factors is a time-consuming progress and its accuracy may be affected by many uncertainties, and the change in drift rates of the relative gravimeters are complex over time in this long-term survey. The reasonable calculation of scale factors and drift rates plays an important role in improving the gravity estimation accuracy. In this paper, based on the least squares, robust least squares, and Bayesian methods, various parameter calculation methods were employed to process this gravity network. The performance and practicality of each method were analyzed in terms of internal and external accuracy. The results indicated that the scale factors calibrated in the baseline field had poor applicability due to insufficient gravity difference, in this case, the scale factors estimated by the adjustment models were more accurate, which weakened the correlation between gravity differences and mutual differences. The drift rates estimated by the Bayesian method were relatively smooth over time, while drift rates estimated using symmetric observations were more practical for the gravimeter with highly variable drift. The weight constraints of observations can be optimized by the robust least squares method, the gravity values obtained by it were more consistent with absolute gravity values than those obtained by the least squares method, and the robust least squares method was recommended to process gravity data in plateau areas.

Highlights

IntroductionA gravity base network forms the basis for gravity surveys and provides accurate gravity field information for mineral resources, geotectonic inversion, geoid refinement, and geodynamic research [1–4]
Introduction published maps and institutional affilA gravity base network forms the basis for gravity surveys and provides accurate gravity field information for mineral resources, geotectonic inversion, geoid refinement, and geodynamic research [1–4]
The results indicated that the gravity points with the highest accuracy were mostly distributed at the largest closed loop of the gravity network (B070, GUID, TODE, GCHA, and A080) or at the edge of the largest closed loop of the gravity network connected with multiple gravity points (QHTJ, B071, A078, XRID, B069, TREN, QHMY, JIAD, HNAN, QHDC, and BUQL)

Summary

Introduction

A gravity base network forms the basis for gravity surveys and provides accurate gravity field information for mineral resources, geotectonic inversion, geoid refinement, and geodynamic research [1–4]. China’s gravity base networks are established by relative gravity survey campaign based on a number of absolute gravity points [5]. The gravity base network of Qinghai Province, China, was established to fulfill the needs of infrastructure construction and geodesy research. The first phase of gravity measurements in this network was performed in the form of hybrid gravimetry, and five absolute gravity points and three relative gravity points were measured from 2017 to 2018. In order to achieve province-wide coverage of this gravity network, the latest phase of gravity survey was conducted from September 2020 to October 2020 using two Scintrex CG-6 and four Scintrex

Results

Discussion

Conclusion