Joint adjustment for large-area, multi-source vertical data: method, validation and application

Abstract

In order to reveal the residual distribution pattern and obtain a wide range of high-accurate vertical movement field, we systematically analyzed the joint geodetic adjustment with vertical rate parameters (denoted as joint adjustment) issues based on the simulated and actual multi-source vertical data. First of all, 1000 sets of simulated leveling data containing rand noise were constructed and processed. The corresponding adjustment results show that network shape and observation interval are two main factors affecting the reliability of the vertical rates. Particularly, vertical rates of the small loops, internal points and loops with longer intervals are prior to those of large loops, edge points and loops with short intervals, respectively. Subsequently, we developed a Helmert adjustment program, which can jointly process leveling data and space geodetic data. Compared with the single leveling data adjustment (SLDA), the joint adjustment results of leveling data and 500 geodetic observations (including 2–4 mm/yr errors) demonstrate that the Helmert adjustment can reduce the residual distribution range by roughly 46%. Finally, both SLDA and Helmert methods are applied to actual survey data in eastern China. We find that the error of the latter approach is decreased by about 48% compared with the former. In summary, the Helmert adjustment method can significantly improve the reliability and accuracy of vertical movement in large area.