Inverse-forward method for heat flow estimation: case study for the Arctic region

The heat flow data are important in many aspects including interpretation of various geophysical observations, solutions of important engineering problems, modelling of the ice dynamics, and related environmental assessment. However, the distribution of the direct measurements is quite heterogeneous...

Full description

Saved in:
Bibliographic Details
Main Authors: Petrunin Aleksey G., Soloviev Anatoly, Sidorov Roman, Gvishiani Alexei
Format: Article
Language:English
Published: Russian Academy of Sciences, The Geophysical Center 2022-12-01
Series:Russian Journal of Earth Sciences
Subjects:
Online Access:http://doi.org/10.2205/2022ES000809
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1849706502285164544
author Petrunin Aleksey G.
Soloviev Anatoly
Sidorov Roman
Gvishiani Alexei
author_facet Petrunin Aleksey G.
Soloviev Anatoly
Sidorov Roman
Gvishiani Alexei
author_sort Petrunin Aleksey G.
collection DOAJ
description The heat flow data are important in many aspects including interpretation of various geophysical observations, solutions of important engineering problems, modelling of the ice dynamics, and related environmental assessment. However, the distribution of the direct measurements is quite heterogeneous over the Earth. Different methods have been developed during past decades to create continuous maps of the geothermal heat flow (GHF). Most of them are based on the principle of similarity of GHF values for the lithosphere with comparable age and tectonic history or inversion of magnetic field data. Probabilistic approach was also used to realize this principle. In this paper, we present a new method for extrapolating the GHF data, based on the inversion of a geophysical data set using optimization problem solution. We use the results of inversion of seismic and magnetic field data into temperature and data from direct heat flow measurements. We use the Arctic as the test area because it includes the lithosphere of different ages, types, and tectonic settings. In result, the knowledge of GHF is important here for various environmental problems. The resulting GHF map obtained well fits to the observed data and clearly reflects the lithospheric domains with different tectonic history and age. The new GHF map constructed in this paper reveals some significant features that were not identified earlier. In particular, these are the increased GHF zones in the Bering Strait, the Chukchi Sea and the residual GHF anomaly in the area of the Mid-Labrador Ridge. The latter was active during the Paleogene.
format Article
id doaj-art-128df85c39e749a0bd16e6cbe742d08e
institution DOAJ
issn 1681-1208
language English
publishDate 2022-12-01
publisher Russian Academy of Sciences, The Geophysical Center
record_format Article
series Russian Journal of Earth Sciences
spelling doaj-art-128df85c39e749a0bd16e6cbe742d08e2025-08-20T03:16:11ZengRussian Academy of Sciences, The Geophysical CenterRussian Journal of Earth Sciences1681-12082022-12-012261910.2205/2022ES000809Inverse-forward method for heat flow estimation: case study for the Arctic regionPetrunin Aleksey G.0Soloviev Anatoly1https://orcid.org/0000-0002-6476-9471Sidorov Roman2Gvishiani Alexei3https://orcid.org/0000-0002-4874-7475Geophysical Center of the Russian Academy of SciencesGeophysical Center RASGeophysical Center of the Russian Academy of SciencesGeophysical Center of the Russian Academy of SciencesThe heat flow data are important in many aspects including interpretation of various geophysical observations, solutions of important engineering problems, modelling of the ice dynamics, and related environmental assessment. However, the distribution of the direct measurements is quite heterogeneous over the Earth. Different methods have been developed during past decades to create continuous maps of the geothermal heat flow (GHF). Most of them are based on the principle of similarity of GHF values for the lithosphere with comparable age and tectonic history or inversion of magnetic field data. Probabilistic approach was also used to realize this principle. In this paper, we present a new method for extrapolating the GHF data, based on the inversion of a geophysical data set using optimization problem solution. We use the results of inversion of seismic and magnetic field data into temperature and data from direct heat flow measurements. We use the Arctic as the test area because it includes the lithosphere of different ages, types, and tectonic settings. In result, the knowledge of GHF is important here for various environmental problems. The resulting GHF map obtained well fits to the observed data and clearly reflects the lithospheric domains with different tectonic history and age. The new GHF map constructed in this paper reveals some significant features that were not identified earlier. In particular, these are the increased GHF zones in the Bering Strait, the Chukchi Sea and the residual GHF anomaly in the area of the Mid-Labrador Ridge. The latter was active during the Paleogene.http://doi.org/10.2205/2022ES000809geothermal heat flow Arctic inversion optimization lithosphere
spellingShingle Petrunin Aleksey G.
Soloviev Anatoly
Sidorov Roman
Gvishiani Alexei
Inverse-forward method for heat flow estimation: case study for the Arctic region
Russian Journal of Earth Sciences
geothermal heat flow
Arctic
inversion
optimization
lithosphere
title Inverse-forward method for heat flow estimation: case study for the Arctic region
title_full Inverse-forward method for heat flow estimation: case study for the Arctic region
title_fullStr Inverse-forward method for heat flow estimation: case study for the Arctic region
title_full_unstemmed Inverse-forward method for heat flow estimation: case study for the Arctic region
title_short Inverse-forward method for heat flow estimation: case study for the Arctic region
title_sort inverse forward method for heat flow estimation case study for the arctic region
topic geothermal heat flow
Arctic
inversion
optimization
lithosphere
url http://doi.org/10.2205/2022ES000809
work_keys_str_mv AT petruninalekseyg inverseforwardmethodforheatflowestimationcasestudyforthearcticregion
AT solovievanatoly inverseforwardmethodforheatflowestimationcasestudyforthearcticregion
AT sidorovroman inverseforwardmethodforheatflowestimationcasestudyforthearcticregion
AT gvishianialexei inverseforwardmethodforheatflowestimationcasestudyforthearcticregion