Low-pass filters are commonly used for the processing of airborne gravity observations. In this paper, for the first time, we include the resulting correlations consistently in the functional and stochastic model of residual least-squares collocation. We demonstrate the necessity of removing high-frequency noise from airborne gravity observations, and derive corresponding parameters for a Gaussian low-pass filter. Thereby, we intend an optimal combination of terrestrial and airborne gravity observations in the mountainous area of Colorado. We validate the combination in the frame of our participation in ‘the 1 cm geoid experiment’. This regional geoid modeling inter-comparison exercise allows the calculation of a reference solution, which is defined as the mean value of 13 independent height anomaly results in this area. Our result performs among the best and with 7.5 mm shows the lowest standard deviation to the reference. From internal validation we furthermore conclude that the input from airborne and terrestrial gravity observations is consistent in large parts of the target area, but not necessarily in the highly mountainous areas. Therefore, the relative weighting between these two data sets turns out to be a main driver for the final result, and is an important factor in explaining the remaining differences between various height anomaly results in this experiment.

中文翻译：

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将航空重力测量数据过滤集成到残差最小二乘搭配中：来自 1 cm 大地水准面实验的示例

低通滤波器通常用于处理机载重力观测。在本文中，我们第一次在残差最小二乘搭配的函数和随机模型中一致地包含了由此产生的相关性。我们证明了从航空重力观测中去除高频噪声的必要性，并推导出高斯低通滤波器的相应参数。因此，我们打算在科罗拉多山区进行地面和空中重力观测的最佳组合。我们在参与“1 厘米大地水准面实验”的框架内验证了该组合。该区域大地水准面建模比对练习允许计算参考解，该解被定义为该区域 13 个独立高度异常结果的平均值。我们的结果表现最好，7.5 毫米显示与参考的最低标准偏差。通过内部验证，我们进一步得出结论，来自空中和地面重力观测的输入在目标区域的大部分地区是一致的，但不一定在高山地区。因此，这两个数据集之间的相对权重被证明是最终结果的主要驱动因素，并且是解释本实验中各种高度异常结果之间存在差异的重要因素。