[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117138-en":3,"doc-seo-117138-105":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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"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},117138,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","TROPOSPHERIC CORRECTION FOR INSAR USING MACHINE LEARNING","Interferometric Synthetic Aperture Radar (InSAR) measures Earth deformation, but its accuracy is constrained by tropospheric delays driven by atmospheric water vapor. The study addresses tropospheric noise by learning to predict zenith total delay from numerical weather prediction inputs. Random forest and artificial neural network models are evaluated across the Continental USA and globally. The neural network achieves the best performance, lowering RMSE by about 30% and improving many interferograms by 30–60% in Pennsylvania and Hawaii. Remaining difficulties appear in regions with highly variable local climate and weather.","Scholars' Mine  \n\n| Masters Theses | Student Theses and Dissertations |\n| --- | --- |\n| Spring 2023\u003Cbr>TROPOSPHERIC CORRECTION FOR INSAR USING MACHINE LEARNING\u003Cbr>Ngo Hi Kenny Yue\u003Cbr>Missouri University of Science and Technology\u003Cbr>Follow this and additional works at: [https://scholarsmine.mst.edu/masters_theses](https://scholarsmine.mst.edu/masters_theses)\u003Cbr> Part of the Geological Engineering Commons\u003Cbr>Department: |  |\n\nRecommended Citation  \nYue, Ngo Hi Kenny, \"TROPOSPHERIC CORRECTION FOR INSAR USING MACHINE LEARNING\" (2023) . Masters Theses. 8155.  \n[https://scholarsmine.mst.edu/masters_theses/8155](https://scholarsmine.mst.edu/masters_theses/8155)  \nThis thesis is brought to you by Scholars' Mine, a service of the Missouri S&T Library and Learning Resources. This work is protected by U. S. Copyright Law. Unauthorized use including reproduction for redistribution requires the permission of the copyright holder. For more information, please contact [scholarsmine@mst.edu](scholarsmine@mst.edu).  \nTROPOSPHERIC CORRECTION FOR INSAR USING MACHINE LEARNING  \nby  \nNGO HI KENNY YUE  \nA THESIS  \nPresented to the Graduate Faculty of the  \nMISSOURI UNIVERSITY OF SCIENCE AND TECHNOLOGY In Partial Fulfillment of the Requirements for the Degree  \nMASTER OF SCIENCE IN GEOLOGICAL ENGINEERING  \n2023  \nApproved by:  \nDr. Jeremy Maurer, Advisor  \nDr. Ryan Smith  \nDr. Ardhendu. Tripathy  \nÓ 2023 Ngo Hi Kenny Yue All Rights Reserved  \niii  \nABSTRACT  \nInterferometric Synthetic Aperture Radar (InSAR) is a popular technique for studying Earth's surface deformation caused by phenomena like earthquakes and subsidence. However, its accuracy is limited by tropospheric delays caused by water vapor in the atmosphere. This limitation can be overcome by using methods that correct for tropospheric noise, such as statistical, empirical, and predictive approaches. This study explores the potential of using machine learning algorithms to predict the zenith total delay caused by tropospheric effects in InSAR measurements. The study employs two different machine learning algorithms, random forest, and neural networks, to learn the relationship between numerical weather prediction model data and InSAR parameters in Continental USA and the globe. The neural network model outperforms both the random forest model and the traditional approach, reducing the RMSE by approximately 30% . The study demonstrates that machine learning algorithms can effectively correct tropospheric noise in most interferograms, resulting in a 30-60% improvement in Pennsylvania and Hawaii. However, the neural network model faces challenges in making predictions in areas with high variability in local climate and weather patterns. Overall, this research presents a promising approach for improving InSAR accuracy by using machine learning algorithms to correct for tropospheric noise.  \niv  \nACKNOWLEDGMENTS  \nI would like to express my sincere gratitude to the following individuals.  \nDr. Jeremy Maurer, for providing me with the opportunity to work on this thesis and for his guidance and mentorship throughout the process.  \nDr. Ryan Smith and Dr. Ardhendu. Tripathy giving me valuable comments and guidance as my committee.  \nJoyce Hsieh, for her unwavering support and belief in me.  \nEveryone in GEM-lab who have provided me with valuable feedback and support.  \nv  \nTABLE OF CONTENTS  \nPage  \nABSTRACT....................................................................................................................... iii  \nACKNOWLEDGMENTS ................................................................................................. iv  \nLIST OF ILLUSTRATIONS ........................................................................................... viii  \nLIST OF TABLES .............................................................................................................. x  \nNOMENCLATURE ..............................................................................................","cbCaikoxtOah64WJ","https://ap.wps.com/l/cbCaikoxtOah64WJ","pdf",18680386,1,92,"English","en",105,"# Abstract\n# Acknowledgments\n# Table of Contents\n# List of Illustrations\n# List of Tables\n# Nomenclature\n# 1. Introduction\n## 1.1. Problem\n## 1.2. Tropospheric Correction Methods\n## 1.3. Zenith Delay\n## 1.4. Motivation\n## 1.5. Proposed Approach\n# 2. Dataset\n## 2.1. NWP Models\n## 2.2. GNSS Data\n## 2.3. Timeframe\n## 2.4. Data Validation\n## 2.5. Accuracy of the Data\n## 2.6. Data Extraction and Preprocessing\n# 3. Method\n## 3.1. Random Forest Model\n## 3.2. Artificial Neural Network Model","[{\"question\":\"Why is tropospheric correction needed for InSAR accuracy?\",\"answer\":\"Tropospheric delays caused by atmospheric water vapor introduce noise that limits how accurately InSAR captures Earth surface deformation.\"},{\"question\":\"What target does the study predict in InSAR measurements?\",\"answer\":\"The study predicts the zenith total delay associated with tropospheric effects in InSAR observations.\"},{\"question\":\"Which machine learning approach performs best and what improvement is reported?\",\"answer\":\"The neural network outperforms random forest and the traditional approach, reducing RMSE by about 30% and improving interferograms by roughly 30–60% in Pennsylvania and Hawaii.\"}]","TROPOSPHERIC CORRECTION FOR INSAR USING MACHINE LEARNING | PDF",1785674068,232,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tropospheric-correction-for-insar-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tropospheric-correction-for-insar-using-machine-learning/117138/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why is tropospheric correction needed for InSAR accuracy?","Question",{"text":75,"@type":76},"Tropospheric delays caused by atmospheric water vapor introduce noise that limits how accurately InSAR captures Earth surface deformation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What target does the study predict in InSAR measurements?",{"text":80,"@type":76},"The study predicts the zenith total delay associated with tropospheric effects in InSAR observations.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning approach performs best and what improvement is reported?",{"text":84,"@type":76},"The neural network outperforms random forest and the traditional approach, reducing RMSE by about 30% and improving interferograms by roughly 30–60% in Pennsylvania and Hawaii.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]