Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography
Source at https://doi.org/10.1109/TGRS.2018.2847026 . In ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two...
Published in: | IEEE Transactions on Geoscience and Remote Sensing |
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Main Authors: | , , , , , , , |
Format: | Article in Journal/Newspaper |
Language: | English |
Published: |
IEEE
2018
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Subjects: | |
Online Access: | https://hdl.handle.net/10037/15387 https://doi.org/10.1109/TGRS.2018.2847026 |
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author | Norberg, J. Vierinen, Juha Roininen, L Orispää, M. Kauristie, K Rideout, W. Coster, A. J. Lehtinen, M |
author_facet | Norberg, J. Vierinen, Juha Roininen, L Orispää, M. Kauristie, K Rideout, W. Coster, A. J. Lehtinen, M |
author_sort | Norberg, J. |
collection | University of Tromsø: Munin Open Research Archive |
container_issue | 12 |
container_start_page | 7009 |
container_title | IEEE Transactions on Geoscience and Remote Sensing |
container_volume | 56 |
description | Source at https://doi.org/10.1109/TGRS.2018.2847026 . In ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two major complications. First, the information provided by measurements is insufficient and additional information is required to obtain a unique solution. Second, with necessary spatial and temporal resolutions, the problem becomes very high dimensional, and hence, computationally infeasible. With Bayesian framework, the required additional information can be given with prior probability distributions. The approach then provides physically quantifiable probabilistic interpretation for all model variables. Here, Gaussian Markov random fields (GMRFs) are used for constructing the prior electron density distribution. The use of GMRF introduces sparsity to the linear system, making the problem computationally feasible. The method is demonstrated over Fennoscandia with measurements from global navigation satellite system (GNSS) and low Earth orbit (LEO) satellite receiver networks, GNSS occultation receivers, LEO satellite Langmuir probes, and ionosonde and incoherent scatter radar measurements. |
format | Article in Journal/Newspaper |
genre | Fennoscandia |
genre_facet | Fennoscandia |
geographic | Langmuir |
geographic_facet | Langmuir |
id | ftunivtroemsoe:oai:munin.uit.no:10037/15387 |
institution | Open Polar |
language | English |
long_lat | ENVELOPE(-67.150,-67.150,-66.967,-66.967) |
op_collection_id | ftunivtroemsoe |
op_container_end_page | 7021 |
op_doi | https://doi.org/10.1109/TGRS.2018.2847026 |
op_relation | IEEE Transactions on Geoscience and Remote Sensing FRIDAID 1586178 doi:10.1109/TGRS.2018.2847026 https://hdl.handle.net/10037/15387 |
op_rights | openAccess |
publishDate | 2018 |
publisher | IEEE |
record_format | openpolar |
spelling | ftunivtroemsoe:oai:munin.uit.no:10037/15387 2025-04-13T14:18:33+00:00 Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography Norberg, J. Vierinen, Juha Roininen, L Orispää, M. Kauristie, K Rideout, W. Coster, A. J. Lehtinen, M 2018-08-22 https://hdl.handle.net/10037/15387 https://doi.org/10.1109/TGRS.2018.2847026 eng eng IEEE IEEE Transactions on Geoscience and Remote Sensing FRIDAID 1586178 doi:10.1109/TGRS.2018.2847026 https://hdl.handle.net/10037/15387 openAccess VDP::Mathematics and natural science: 400::Geosciences: 450::Other geosciences: 469 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Andre geofag: 469 VDP::Mathematics and natural science: 400::Physics: 430::Astrophysics astronomy: 438 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Astrofysikk astronomi: 438 Bayesian Gaussian Markov random fields (GMRFs) Ionospheric tomography Multi-instrument Journal article Tidsskriftartikkel Peer reviewed 2018 ftunivtroemsoe https://doi.org/10.1109/TGRS.2018.2847026 2025-03-14T05:17:56Z Source at https://doi.org/10.1109/TGRS.2018.2847026 . In ionospheric tomography, the atmospheric electron density is reconstructed from different electron density related measurements, most often from ground-based measurements of satellite signals. Typically, ionospheric tomography suffers from two major complications. First, the information provided by measurements is insufficient and additional information is required to obtain a unique solution. Second, with necessary spatial and temporal resolutions, the problem becomes very high dimensional, and hence, computationally infeasible. With Bayesian framework, the required additional information can be given with prior probability distributions. The approach then provides physically quantifiable probabilistic interpretation for all model variables. Here, Gaussian Markov random fields (GMRFs) are used for constructing the prior electron density distribution. The use of GMRF introduces sparsity to the linear system, making the problem computationally feasible. The method is demonstrated over Fennoscandia with measurements from global navigation satellite system (GNSS) and low Earth orbit (LEO) satellite receiver networks, GNSS occultation receivers, LEO satellite Langmuir probes, and ionosonde and incoherent scatter radar measurements. Article in Journal/Newspaper Fennoscandia University of Tromsø: Munin Open Research Archive Langmuir ENVELOPE(-67.150,-67.150,-66.967,-66.967) IEEE Transactions on Geoscience and Remote Sensing 56 12 7009 7021 |
spellingShingle | VDP::Mathematics and natural science: 400::Geosciences: 450::Other geosciences: 469 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Andre geofag: 469 VDP::Mathematics and natural science: 400::Physics: 430::Astrophysics astronomy: 438 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Astrofysikk astronomi: 438 Bayesian Gaussian Markov random fields (GMRFs) Ionospheric tomography Multi-instrument Norberg, J. Vierinen, Juha Roininen, L Orispää, M. Kauristie, K Rideout, W. Coster, A. J. Lehtinen, M Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title | Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title_full | Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title_fullStr | Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title_full_unstemmed | Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title_short | Gaussian Markov random field priors in ionospheric 3D multi-instrument tomography |
title_sort | gaussian markov random field priors in ionospheric 3d multi-instrument tomography |
topic | VDP::Mathematics and natural science: 400::Geosciences: 450::Other geosciences: 469 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Andre geofag: 469 VDP::Mathematics and natural science: 400::Physics: 430::Astrophysics astronomy: 438 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Astrofysikk astronomi: 438 Bayesian Gaussian Markov random fields (GMRFs) Ionospheric tomography Multi-instrument |
topic_facet | VDP::Mathematics and natural science: 400::Geosciences: 450::Other geosciences: 469 VDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Andre geofag: 469 VDP::Mathematics and natural science: 400::Physics: 430::Astrophysics astronomy: 438 VDP::Matematikk og Naturvitenskap: 400::Fysikk: 430::Astrofysikk astronomi: 438 Bayesian Gaussian Markov random fields (GMRFs) Ionospheric tomography Multi-instrument |
url | https://hdl.handle.net/10037/15387 https://doi.org/10.1109/TGRS.2018.2847026 |