{"title":"Minimum-variance-based outlier detection method using forward-search model error in geodetic networks","authors":"U. M. Durdag","doi":"10.5194/gmd-17-2187-2024","DOIUrl":null,"url":null,"abstract":"Abstract. Geodetic observations are crucial for monitoring landslides, crustal movements, and volcanic activity. They are often integrated with data from interdisciplinary studies, including paleo-seismological, geological, and interferometric synthetic aperture radar observations, to analyze earthquake hazards. However, outliers in geodetic observations can significantly impact the accuracy of estimation results if not reliably identified. Therefore, assessing the outlier detection model's reliability is imperative to ensure accurate interpretations. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss–Markov model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible of outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and eliminates the interdependence of decisions made in iterations. The primary purpose of this study is to seek an efficient outlier detection model in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the mean success rate (MSR) indicator of different types and magnitudes of outliers. Thereby, this model enhances the MSR by almost 40 %–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers. Besides, the proposed model is 20 %–30 % more successful than the others in the low-controllability observations of the leveling network.\n","PeriodicalId":12799,"journal":{"name":"Geoscientific Model Development","volume":null,"pages":null},"PeriodicalIF":4.0000,"publicationDate":"2024-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Geoscientific Model Development","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.5194/gmd-17-2187-2024","RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"GEOSCIENCES, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
Abstract. Geodetic observations are crucial for monitoring landslides, crustal movements, and volcanic activity. They are often integrated with data from interdisciplinary studies, including paleo-seismological, geological, and interferometric synthetic aperture radar observations, to analyze earthquake hazards. However, outliers in geodetic observations can significantly impact the accuracy of estimation results if not reliably identified. Therefore, assessing the outlier detection model's reliability is imperative to ensure accurate interpretations. Conventional and robust methods are based on the additive bias model, which may cause type-I and type-II errors. However, outliers can be regarded as additional unknown parameters in the Gauss–Markov model. It is based on modeling the outliers as unknown parameters, considering as many combinations as possible of outliers selected from the observation set. In addition, this method is expected to be more effective than conventional methods as it is based on the principle of minimal variance and eliminates the interdependence of decisions made in iterations. The primary purpose of this study is to seek an efficient outlier detection model in the geodetic networks. The efficiency of the proposed model was measured and compared with the robust and conventional methods by the mean success rate (MSR) indicator of different types and magnitudes of outliers. Thereby, this model enhances the MSR by almost 40 %–45 % compared to the Baarda and Danish (with the variance unknown case) method for multiple outliers. Besides, the proposed model is 20 %–30 % more successful than the others in the low-controllability observations of the leveling network.
期刊介绍:
Geoscientific Model Development (GMD) is an international scientific journal dedicated to the publication and public discussion of the description, development, and evaluation of numerical models of the Earth system and its components. The following manuscript types can be considered for peer-reviewed publication:
* geoscientific model descriptions, from statistical models to box models to GCMs;
* development and technical papers, describing developments such as new parameterizations or technical aspects of running models such as the reproducibility of results;
* new methods for assessment of models, including work on developing new metrics for assessing model performance and novel ways of comparing model results with observational data;
* papers describing new standard experiments for assessing model performance or novel ways of comparing model results with observational data;
* model experiment descriptions, including experimental details and project protocols;
* full evaluations of previously published models.