Version 1
: Received: 22 December 2023 / Approved: 25 December 2023 / Online: 25 December 2023 (16:05:08 CET)
How to cite:
Matsuoka, M.; Rofatto, V.; Assunção, J.; Silva, L.; Klein, I.; Camargo, P. A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis. Preprints2023, 2023121858. https://doi.org/10.20944/preprints202312.1858.v1
Matsuoka, M.; Rofatto, V.; Assunção, J.; Silva, L.; Klein, I.; Camargo, P. A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis. Preprints 2023, 2023121858. https://doi.org/10.20944/preprints202312.1858.v1
Matsuoka, M.; Rofatto, V.; Assunção, J.; Silva, L.; Klein, I.; Camargo, P. A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis. Preprints2023, 2023121858. https://doi.org/10.20944/preprints202312.1858.v1
APA Style
Matsuoka, M., Rofatto, V., Assunção, J., Silva, L., Klein, I., & Camargo, P. (2023). A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis. Preprints. https://doi.org/10.20944/preprints202312.1858.v1
Chicago/Turabian Style
Matsuoka, M., Ivandro Klein and Paulo Camargo. 2023 "A New Sequential Statistical Test Procedure from Observation Differences for Geodetic Deformation Analysis" Preprints. https://doi.org/10.20944/preprints202312.1858.v1
Abstract
One of the main challenges in geodetic deformation analysis is to infer whether the geometry of some engineering structures or zones of natural hazards has changed from their initial state or not. The pillar that supports such an analysis is tightly based on the fundamentals of statistical hypothesis testing. The null hypothesis model indicates that no displacement has occurred. It is tested against a class of alternative models, which stipulate different displacement patterns. In this contribution, we present an innovative geodetic displacement detection which integrates combinatorial analysis and likelihood ratio tests into a sequential procedure for the case where the differences between observations of two epochs are in play. This framework is applied and investigated to two test scenarios: a synthetic and a real simulated trilateration network. From a statistical point of view, our approach is rigorous, because the alternative model can identify simultaneously more than one unstable point. In addition, the relationship between the unknown parameters and the observations is always linear, even if the problem manifests itself as non-linear. Consequently, we avoid a potential loss of statistical test power due to the model linearization. In addition, the proposed method controls the false positive rate efficiently. One of the problems that arise here is also related to the selection of the maximum number of points to be considered in the test procedure ( ). Here, we provide an innovative methodology based on rank computation of the design matrices to define , which can even be extended to the problem of outlier detection. Determining avoids the problem of having non-separable models in identifying unstable points. The algorithms and data are available in the repository.
Keywords
Deformation; Displacement; Monitoring; Statistical Testing; Quality Control; Monte Carlo
Subject
Environmental and Earth Sciences, Other
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
,
