Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography
Abstract 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 p...
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ftunivoulu:oai:oulu.fi:nbnfi-fe2018121751126 2023-07-30T04:03:26+02:00 Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography Norberg, J. (Johannes) Vierinen, J. (Juha) Roininen, L. (Lassi) Orispää, M. (Mikko) Kauristie, K. (Kirsti) Rideout, W. C. (William C.) Coster, A. J. (Anthea J.) Lehtinen, M. S. (Markku S.) 2018 application/pdf http://urn.fi/urn:nbn:fi-fe2018121751126 eng eng Institute of Electrical and Electronics Engineers info:eu-repo/semantics/altIdentifier/pissn/0196-2892 info:eu-repo/semantics/altIdentifier/eissn/1558-0644 info:eu-repo/semantics/openAccess © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Bayesian Gaussian Markov random fields (GMRFs) ionospheric tomography multi-instrument info:eu-repo/semantics/article info:eu-repo/semantics/acceptedVersion 2018 ftunivoulu 2023-07-08T20:00:39Z Abstract 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 Jultika - University of Oulu repository Langmuir ENVELOPE(-67.150,-67.150,-66.967,-66.967) |
institution |
Open Polar |
collection |
Jultika - University of Oulu repository |
op_collection_id |
ftunivoulu |
language |
English |
topic |
Bayesian Gaussian Markov random fields (GMRFs) ionospheric tomography multi-instrument |
spellingShingle |
Bayesian Gaussian Markov random fields (GMRFs) ionospheric tomography multi-instrument Norberg, J. (Johannes) Vierinen, J. (Juha) Roininen, L. (Lassi) Orispää, M. (Mikko) Kauristie, K. (Kirsti) Rideout, W. C. (William C.) Coster, A. J. (Anthea J.) Lehtinen, M. S. (Markku S.) Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
topic_facet |
Bayesian Gaussian Markov random fields (GMRFs) ionospheric tomography multi-instrument |
description |
Abstract 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 |
author |
Norberg, J. (Johannes) Vierinen, J. (Juha) Roininen, L. (Lassi) Orispää, M. (Mikko) Kauristie, K. (Kirsti) Rideout, W. C. (William C.) Coster, A. J. (Anthea J.) Lehtinen, M. S. (Markku S.) |
author_facet |
Norberg, J. (Johannes) Vierinen, J. (Juha) Roininen, L. (Lassi) Orispää, M. (Mikko) Kauristie, K. (Kirsti) Rideout, W. C. (William C.) Coster, A. J. (Anthea J.) Lehtinen, M. S. (Markku S.) |
author_sort |
Norberg, J. (Johannes) |
title |
Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
title_short |
Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
title_full |
Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
title_fullStr |
Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
title_full_unstemmed |
Gaussian Markov random field priors in ionospheric 3-D multi-instrument tomography |
title_sort |
gaussian markov random field priors in ionospheric 3-d multi-instrument tomography |
publisher |
Institute of Electrical and Electronics Engineers |
publishDate |
2018 |
url |
http://urn.fi/urn:nbn:fi-fe2018121751126 |
long_lat |
ENVELOPE(-67.150,-67.150,-66.967,-66.967) |
geographic |
Langmuir |
geographic_facet |
Langmuir |
genre |
Fennoscandia |
genre_facet |
Fennoscandia |
op_relation |
info:eu-repo/semantics/altIdentifier/pissn/0196-2892 info:eu-repo/semantics/altIdentifier/eissn/1558-0644 |
op_rights |
info:eu-repo/semantics/openAccess © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
_version_ |
1772814446202191872 |