DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf
We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various c...
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ftfrontimediafig:oai:figshare.com:article/22187503 2024-09-15T17:57:58+00:00 DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf Michael S. Zhdanov Michael Jorgensen Mo Tao 2023-02-28T04:42:27Z https://doi.org/10.3389/feart.2023.1127597.s001 https://figshare.com/articles/dataset/DataSheet1_Probabilistic_approach_to_Gramian_inversion_of_multiphysics_data_pdf/22187503 unknown doi:10.3389/feart.2023.1127597.s001 https://figshare.com/articles/dataset/DataSheet1_Probabilistic_approach_to_Gramian_inversion_of_multiphysics_data_pdf/22187503 CC BY 4.0 Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change 3D inversion probabilistic multiphysics gravity magnetic Dataset 2023 ftfrontimediafig https://doi.org/10.3389/feart.2023.1127597.s001 2024-08-19T06:19:54Z We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs. Dataset Barents Sea Nordkapp Nordkapp Basin Frontiers: Figshare |
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Open Polar |
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Frontiers: Figshare |
op_collection_id |
ftfrontimediafig |
language |
unknown |
topic |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change 3D inversion probabilistic multiphysics gravity magnetic |
spellingShingle |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change 3D inversion probabilistic multiphysics gravity magnetic Michael S. Zhdanov Michael Jorgensen Mo Tao DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
topic_facet |
Solid Earth Sciences Climate Science Atmospheric Sciences not elsewhere classified Exploration Geochemistry Inorganic Geochemistry Isotope Geochemistry Organic Geochemistry Geochemistry not elsewhere classified Igneous and Metamorphic Petrology Ore Deposit Petrology Palaeontology (incl. Palynology) Structural Geology Tectonics Volcanology Geology not elsewhere classified Seismology and Seismic Exploration Glaciology Hydrogeology Natural Hazards Quaternary Environments Earth Sciences not elsewhere classified Evolutionary Impacts of Climate Change 3D inversion probabilistic multiphysics gravity magnetic |
description |
We consider a probabilistic approach to the joint inversion of multiphysics data based on Gramian constraints. The multiphysics geophysical survey represents the most effective technique for geophysical exploration because different physical data reflect distinct physical properties of the various components of the geological system. By joint inversion of the multiphysics data, one can produce enhanced subsurface images of the physical properties distribution, which improves our ability to explore natural resources. One powerful method of joint inversion is based on Gramian constraints. This technique enforces the relationships between different model parameters during the inversion process. We demonstrate that the Gramian can be interpreted as a determinant of the covariance matrix between different physical models representing the subsurface geology in the framework of the probabilistic approach to inversion theory. This interpretation opens the way to use all the power of the modern probability theory and statistics in developing novel methods for joint inversion of the multiphysics data. We apply the developed joint inversion methodology to inversion of gravity gradiometry and magnetic data in the Nordkapp Basin, Barents Sea to image salt diapirs. |
format |
Dataset |
author |
Michael S. Zhdanov Michael Jorgensen Mo Tao |
author_facet |
Michael S. Zhdanov Michael Jorgensen Mo Tao |
author_sort |
Michael S. Zhdanov |
title |
DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
title_short |
DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
title_full |
DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
title_fullStr |
DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
title_full_unstemmed |
DataSheet1_Probabilistic approach to Gramian inversion of multiphysics data.pdf |
title_sort |
datasheet1_probabilistic approach to gramian inversion of multiphysics data.pdf |
publishDate |
2023 |
url |
https://doi.org/10.3389/feart.2023.1127597.s001 https://figshare.com/articles/dataset/DataSheet1_Probabilistic_approach_to_Gramian_inversion_of_multiphysics_data_pdf/22187503 |
genre |
Barents Sea Nordkapp Nordkapp Basin |
genre_facet |
Barents Sea Nordkapp Nordkapp Basin |
op_relation |
doi:10.3389/feart.2023.1127597.s001 https://figshare.com/articles/dataset/DataSheet1_Probabilistic_approach_to_Gramian_inversion_of_multiphysics_data_pdf/22187503 |
op_rights |
CC BY 4.0 |
op_doi |
https://doi.org/10.3389/feart.2023.1127597.s001 |
_version_ |
1810434187820269568 |