[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126296-en":3,"doc-seo-126296-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126296,2336475104736,"Quinn","https://ap-avatar.wpscdn.com/avatar/22000c4c5e0e5b17e70?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786591360781797222",8,"Research & Report","Leveraging Machine Learning for Advanced Geodetic Data Analysis - EGU General Assembly 2025","Geodetic data analysis traditionally uses geophysical models and statistical methods to quantify Earth deformation, mitigate atmospheric impacts, and refine measurement uncertainties. With growing volumes and complexity of observations, machine learning can better capture non-linear site motions by learning environmental effects and correcting influences not covered by existing models. The study examines ML for geodetic processing, with emphasis on station height variations from non-tidal loading in VLBI, using meteorological and land-surface state variables. Ensemble and neural network approaches are compared by strengths and limitations for displacement modelling.","EGU25-7827, updated on 10 Sep 2025  \n[https://doi.org/10.5194/egusphere-egu25-7827](https://doi.org/10.5194/egusphere-egu25-7827)[ ](https://doi.org/10.5194/egusphere-egu25-7827)EGU General Assembly 2025  \n© Author(s) 2025. This work is distributed under the Creative Commons Attribution 4.0 License.  \nLeveraging Machine Learning for Advanced Geodetic Data Analysis Shivangi Singh1, Johannes Böhm2, Hana Krásná2, Sigrid Böhm2, Nagarajan Balasubramanian1, and Onkar Dikshit1  \n1 Indian Institute of Technology Kanpur, Department of Civil Engineering, Kanpur, India 2Technische Universität Wien, Department of Geodesy and Geoinformation, Vienna, Austria  \nGeodetic data analysis has traditionally relied on geophysical models and statistical methods to quantify Earth's deformation, correct for atmospheric effects, and refine measurement uncertainties. However, with the increasing volume and complexity of geodetic observations, machine learning (ML) may offer a better alternative for modelling non-linear motions of geodetic sites by capturing environmental effects and providing corrections for unmodelled influences.  \nML has applications across various domains of geodesy, including coordinate time series analysis, geophysical deformation modelling, atmospheric and hydrological loading corrections, prediction of Earth orientation parameters, and tropospheric delay modelling. This study explores the use of ML techniques in geodetic data processing, focusing on modelling station height variations due to non-tidal loading (NTL) and other unmodelled effects in Very Long Baseline Interferometry (VLBI) data analysis using meteorological and land surface state variables.  \nDifferent ML approaches, including ensemble methods and neural networks, are examined to understand how well they can model displacement of geodetic sites due to meteorological and land surface state variables responsible for the redistribution of geophysical fluids on Earth. The study aims to compare these methods, highlighting their strengths and limitations in geodetic applications. By providing a broad perspective on ML integration in geodesy, this work contributes to the ongoing discussion on data-driven approaches for improving geodetic modelling and analysis.","cbCaikvtx5xBIuVf","https://ap.wps.com/l/cbCaikvtx5xBIuVf","pdf",291227,6,1,"English","en",105,"# Introduction\n# Machine Learning Applications in Geodesy\n## Target Use Case: VLBI and Non-Tidal Loading\n# Methods and Model Comparison\n## Ensemble Methods and Neural Networks\n# Contributions and Discussion","[{\"question\":\"Why is machine learning considered for advanced geodetic data analysis?\",\"answer\":\"Machine learning can model non-linear motions more effectively as geodetic observations become larger and more complex, by learning environmental effects and providing corrections for unmodelled influences.\"},{\"question\":\"What specific geodetic problem does the study focus on?\",\"answer\":\"It focuses on modelling station height variations caused by non-tidal loading and other unmodelled effects in Very Long Baseline Interferometry (VLBI) data analysis.\"},{\"question\":\"Which machine learning approaches are evaluated in the work?\",\"answer\":\"The study examines ensemble methods and neural networks to determine how well they model geodetic displacements using meteorological and land-surface state variables.\"}]","Leveraging Machine Learning for Advanced Geodetic Data Analysis - 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