Like many geophysical observations, relative gravity (RG) measurements are affected by random errors, systematic errors, and occasional blunders. When RG measurements are used to build large gravity networks in remote areas under adverse environmental or logistical conditions (such as extreme temperatures, heavy precipitation, rugged terrain, difficult or dangerous roads, and high altitudes), it is more likely for significant errors to occur and accumulate. Therefore, obtaining accurate gravity estimates at regional gravity networks largely depends on defensive data collection protocols and robust adjustment techniques. In this work, we present a measurement field protocol based on highly redundant observation patterns, and a two-step least squares adjustment scheme implemented as a MATLAB package. This software helps us identify blunders, mitigates the impact of random errors, and downweights or removes outlier observations. The methodology also guarantees that adjusted gravity values have well-constrained standard error estimates. We illustrate the capabilities of our approach through the case study of the Bolivian gravity network, where we determined the acceleration due to gravity at 2548 stations that spread over difficult and sometimes extreme environments, with a typical level of uncertainty of 0.10–0.15 mGal.

与许多地球物理观测一样，相对重力 (RG) 测量会受到随机误差、系统误差和偶尔错误的影响。当使用RG测量在恶劣的环境或物流条件（例如极端温度、强降水、崎岖的地形、困难或危险的道路以及高海拔）的偏远地区建立大型重力网时，更容易出现重大误差并积累。因此，在区域重力网络中获得准确的重力估计很大程度上取决于防御性数据收集协议和稳健的调整技术。在这项工作中，我们提出了一种基于高度冗余观测模式的测量现场协议，以及作为 MATLAB 包实现的两步最小二乘调整方案。该软件帮助我们识别错误，减轻随机错误的影响，并降低权重或删除异常观测值。该方法还保证调整后的重力值具有良好约束的标准误差估计。我们通过玻利维亚重力网络的案例研究来说明我们方法的功能，其中我们确定了 2548 个站点的重力加速度，这些站点分布在困难且有时是极端的环境中，典型的不确定性水平为 0.10-0.15 mGal。