Predicted Antarctic Heat Flow and Uncertainties using Machine Learning
We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We applied a Gradient Boosted Regression Tre...
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ftpangaea:oai:pangaea.de:doi:10.1594/PANGAEA.930237 2024-09-15T17:44:26+00:00 Predicted Antarctic Heat Flow and Uncertainties using Machine Learning Lösing, Mareen Ebbing, Jörg 2021 text/tab-separated-values, 4 data points https://doi.pangaea.de/10.1594/PANGAEA.930237 https://doi.org/10.1594/PANGAEA.930237 en eng PANGAEA Lösing, Mareen; Ebbing, J (2021): Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach. Journal of Geophysical Research: Solid Earth, 126(6), https://doi.org/10.1029/2020JB021499 https://doi.pangaea.de/10.1594/PANGAEA.930237 https://doi.org/10.1594/PANGAEA.930237 CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess Antarctica Binary Object Binary Object (File Size) Binary Object (Media Type) Description Gondwana heat flow machine learning Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas SPP1158 dataset 2021 ftpangaea https://doi.org/10.1594/PANGAEA.93023710.1029/2020JB021499 2024-07-24T02:31:34Z We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We applied a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. In Antarctica, only a sparse number of direct GHF measurements are available, and therefore, in addition to the global models, we explore the use of regional data sets of Antarctica as well as its tectonic Gondwana neighbors to refine the predictions. We hereby demonstrated the need for adding reliable data to the machine learning approach. Here, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2 and showing visible connections to the conjugate margins in Australia, Africa, and India. Also, the data set contains minimum and maximum heat flow values and maximum absolute differences, resulting from calculating three additional heat flow models with different feature set-ups to assess the direct uncertainties. Dataset Antarc* Antarctic Antarctica Sea ice PANGAEA - Data Publisher for Earth & Environmental Science |
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Open Polar |
collection |
PANGAEA - Data Publisher for Earth & Environmental Science |
op_collection_id |
ftpangaea |
language |
English |
topic |
Antarctica Binary Object Binary Object (File Size) Binary Object (Media Type) Description Gondwana heat flow machine learning Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas SPP1158 |
spellingShingle |
Antarctica Binary Object Binary Object (File Size) Binary Object (Media Type) Description Gondwana heat flow machine learning Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas SPP1158 Lösing, Mareen Ebbing, Jörg Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
topic_facet |
Antarctica Binary Object Binary Object (File Size) Binary Object (Media Type) Description Gondwana heat flow machine learning Priority Programme 1158 Antarctic Research with Comparable Investigations in Arctic Sea Ice Areas SPP1158 |
description |
We predicted Antarctic Geothermal Heat Flow (GHF) using a machine learning approach. The adopted approach estimates GHF from multiple geophysical and geological data sets, assuming that GHF is substantially related to the geodynamic setting of the plates. We applied a Gradient Boosted Regression Tree algorithm to find an optimal prediction model relating GHF to the observables. In Antarctica, only a sparse number of direct GHF measurements are available, and therefore, in addition to the global models, we explore the use of regional data sets of Antarctica as well as its tectonic Gondwana neighbors to refine the predictions. We hereby demonstrated the need for adding reliable data to the machine learning approach. Here, we present a new geothermal heat flow map, which exhibits intermediate values compared to previous models, ranging from 35 to 156 mW/m2 and showing visible connections to the conjugate margins in Australia, Africa, and India. Also, the data set contains minimum and maximum heat flow values and maximum absolute differences, resulting from calculating three additional heat flow models with different feature set-ups to assess the direct uncertainties. |
format |
Dataset |
author |
Lösing, Mareen Ebbing, Jörg |
author_facet |
Lösing, Mareen Ebbing, Jörg |
author_sort |
Lösing, Mareen |
title |
Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
title_short |
Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
title_full |
Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
title_fullStr |
Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
title_full_unstemmed |
Predicted Antarctic Heat Flow and Uncertainties using Machine Learning |
title_sort |
predicted antarctic heat flow and uncertainties using machine learning |
publisher |
PANGAEA |
publishDate |
2021 |
url |
https://doi.pangaea.de/10.1594/PANGAEA.930237 https://doi.org/10.1594/PANGAEA.930237 |
genre |
Antarc* Antarctic Antarctica Sea ice |
genre_facet |
Antarc* Antarctic Antarctica Sea ice |
op_relation |
Lösing, Mareen; Ebbing, J (2021): Predicting Geothermal Heat Flow in Antarctica With a Machine Learning Approach. Journal of Geophysical Research: Solid Earth, 126(6), https://doi.org/10.1029/2020JB021499 https://doi.pangaea.de/10.1594/PANGAEA.930237 https://doi.org/10.1594/PANGAEA.930237 |
op_rights |
CC-BY-4.0: Creative Commons Attribution 4.0 International Access constraints: unrestricted info:eu-repo/semantics/openAccess |
op_doi |
https://doi.org/10.1594/PANGAEA.93023710.1029/2020JB021499 |
_version_ |
1810492013448003584 |