Design of Geodetic Monitoring Networks by Minimal Detectable Displacement Evaluation under Confidence and Sensitivity Analysis

This study investigated the differences between minimal detectable displacement (MDD) values derived from confidence and sensitivity analysis, focusing on the a priori analysis of geodetic monitoring networks using a unified approach from the literature. Several aspects were examined: network configuration (number of observations), spatial dimension of network points, and its functional model. Our results show that these aspects have a major role in the minimal detectable displacement values for both confidence and sensitivity approaches, thereby influencing the network’s capacity to detect displacements. Specifically, increasing the number of observations reduces the discrepancies between the displacements detected by the confidence and sensitivity approaches. We also show that geodetic networks with the same parameter dimensionality but different spatial dimensions [one-dimensional (1D), two-dimensional (2D), or three-dimensional (3D)] have different capabilities for detecting displacements. Increasing the network redundancy should be more efficient for 1D networks than for 3D networks. In addition, the larger the spatial dimension of the network, the smaller is the maximum MDD value for a given h=rank(Cd^). Lastly, we present an example of Global Navigation Satellite System (GNSS) network design for geodetic monitoring, demonstrating a direct relationship between time span and the Type 2 error probability.