Ionosphere and Plasmasphere Simultaneous Tomography Constrained by A Deep Learning Topside Model

Ionosphere and Plasmasphere Simultaneous Tomography Constrained by A Deep Learning Topside Model | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Ionosphere and Plasmasphere Simultaneous Tomography Constrained by A Deep Learning Topside Model Changzhi Zhai, Yibin Yao, Jian Kong, Yutian Chen, Wei Liang, Shenquan Tang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6638907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 23 Feb, 2026 Read the published version in GPS Solutions → Version 1 posted 11 You are reading this latest preprint version Abstract The plasmasphere is an important part of the solar-terrestrial system and contributes prominently to the Global Navigation Satellite System (GNSS) Total Electron Content (TEC). Due to the large discrepancy in electron density (Ne) magnitudes between the ionosphere and plasmasphere, as well as the asynchronous variations between the two, traditional tomography methods face significant challenges in simultaneously reconstructing the three-dimensional structures of both the ionosphere and plasmasphere. This paper introduces a novel Ionosphere and Plasmasphere Simultaneous Tomography (IPST) method constrained by a deep learning topside model. The model, based on Long Short-Term Memory (LSTM) neural networks and trained using electron density observations from Alouette and ISIS satellites, was combined with Thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM) simulations to infer the topside constraints for the tomography. The proposed method reconstructs ionospheric and plasmaspheric electron density profiles with higher accuracy compared to traditional methods such as Improved Constrained Simultaneous Iterative Reconstruction Technique (ICSIRT) and ICSIRT for ionosphere and plasmasphere (ICSIRT-IP). Simulation results show that IPST achieves lower errors, particularly above 1000 km altitude. Real data experiments during both quiet and storm days confirm that IPST produces more accurate Ne profiles and F2 peak density (NmF2) values, as well as smaller absolute errors in Slant TEC (STEC), compared to ICSIRT-IP. The reconstructed electron density from IPST showed greater consistency with ionosonde and DMSP satellite observations, with Root Mean Square Error (RMSE) reductions of 31% in NmF2 and 36% in STEC. These results highlight the potential of the IPST method for improving ionosphere and plasmasphere electron density reconstructions. Global navigation satellite system (GNSS) Computerized ionospheric tomography plasmasphere long short-term memory (LSTM) Upper Transition Height (UTH) Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 23 Feb, 2026 Read the published version in GPS Solutions → Version 1 posted Editorial decision: Revision requested 27 Jul, 2025 Reviews received at journal 25 Jul, 2025 Reviewers agreed at journal 06 Jul, 2025 Reviews received at journal 20 Jun, 2025 Reviewers agreed at journal 09 Jun, 2025 Reviews received at journal 08 Jun, 2025 Reviewers agreed at journal 02 Jun, 2025 Reviewers invited by journal 02 Jun, 2025 Editor assigned by journal 24 May, 2025 Submission checks completed at journal 12 May, 2025 First submitted to journal 11 May, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6638907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":465538249,"identity":"6de59b3f-849f-413f-a753-196089faadc1","order_by":0,"name":"Changzhi 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