Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada
SUMMARY Understanding deep crustal structure can provide us with insights into tectonic processes and how they affect the geological record. The deep crustal structure can be studied using a suite of seismological techniques such as receiver function analysis, body and surface wave tomography. Using...
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Oxford University Press (OUP)
2022
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Online Access: | http://dx.doi.org/10.1093/gji/ggac456 https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggac456/47197072/ggac456.pdf https://academic.oup.com/gji/article-pdf/233/1/359/48351688/ggac456.pdf |
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croxfordunivpr:10.1093/gji/ggac456 2024-05-19T07:37:56+00:00 Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada Kharita, Akash Gilligan, Amy 2022 http://dx.doi.org/10.1093/gji/ggac456 https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggac456/47197072/ggac456.pdf https://academic.oup.com/gji/article-pdf/233/1/359/48351688/ggac456.pdf en eng Oxford University Press (OUP) https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model Geophysical Journal International volume 233, issue 1, page 359-375 ISSN 0956-540X 1365-246X journal-article 2022 croxfordunivpr https://doi.org/10.1093/gji/ggac456 2024-05-02T09:32:02Z SUMMARY Understanding deep crustal structure can provide us with insights into tectonic processes and how they affect the geological record. The deep crustal structure can be studied using a suite of seismological techniques such as receiver function analysis, body and surface wave tomography. Using models of crustal structure derived from these methods, it is possible to delineate tectonic boundaries and regions that may have been affected by similar processes. However, often velocity models are grouped in a somewhat subjective manner, potentially meaning that some geological insight may be missed. Cluster analysis, based on unsupervised machine learning, can be used to more objectively group similar velocity profiles and, thus, put additional constraints on the deep crustal structure. In this study, we apply hierarchical agglomerative clustering to the shear wave velocity profiles obtained by previous studies focused on the region from the joint inversion of receiver functions and surface wave dispersion data at 59 sites surrounding the Hudson Bay. This location provides an ideal natural laboratory to study the Precambrian tectonic processes, including the 1.8Ga Trans-Hudson Orogen. We use Ward linkage to define the distance between clusters, as it gives the most physically realistic results, and after testing the number of clusters from 2 to 10, we find there are 5 main stable clusters of velocity models. We then compare our results with different inversion parameters, clustering schemes (K-means and GMM), as well as results obtained for profiles from receiver functions in different azimuths and find that, overall, the clustering results are consistent. The clusters that form correlate well with the surface geology, crustal thickness, regional tectonics and previous geophysical studies concentrated on specific regions. The profiles in the Archean domains (Rae, Hearne and Superior) are clearly distinguished from the profiles in regions influenced by Proterozoic orogenic events (Southern Baffin Island and ... Article in Journal/Newspaper Baffin Island Baffin Hudson Bay Oxford University Press Geophysical Journal International 233 1 359 375 |
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Open Polar |
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Oxford University Press |
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croxfordunivpr |
language |
English |
description |
SUMMARY Understanding deep crustal structure can provide us with insights into tectonic processes and how they affect the geological record. The deep crustal structure can be studied using a suite of seismological techniques such as receiver function analysis, body and surface wave tomography. Using models of crustal structure derived from these methods, it is possible to delineate tectonic boundaries and regions that may have been affected by similar processes. However, often velocity models are grouped in a somewhat subjective manner, potentially meaning that some geological insight may be missed. Cluster analysis, based on unsupervised machine learning, can be used to more objectively group similar velocity profiles and, thus, put additional constraints on the deep crustal structure. In this study, we apply hierarchical agglomerative clustering to the shear wave velocity profiles obtained by previous studies focused on the region from the joint inversion of receiver functions and surface wave dispersion data at 59 sites surrounding the Hudson Bay. This location provides an ideal natural laboratory to study the Precambrian tectonic processes, including the 1.8Ga Trans-Hudson Orogen. We use Ward linkage to define the distance between clusters, as it gives the most physically realistic results, and after testing the number of clusters from 2 to 10, we find there are 5 main stable clusters of velocity models. We then compare our results with different inversion parameters, clustering schemes (K-means and GMM), as well as results obtained for profiles from receiver functions in different azimuths and find that, overall, the clustering results are consistent. The clusters that form correlate well with the surface geology, crustal thickness, regional tectonics and previous geophysical studies concentrated on specific regions. The profiles in the Archean domains (Rae, Hearne and Superior) are clearly distinguished from the profiles in regions influenced by Proterozoic orogenic events (Southern Baffin Island and ... |
format |
Article in Journal/Newspaper |
author |
Kharita, Akash Gilligan, Amy |
spellingShingle |
Kharita, Akash Gilligan, Amy Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
author_facet |
Kharita, Akash Gilligan, Amy |
author_sort |
Kharita, Akash |
title |
Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
title_short |
Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
title_full |
Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
title_fullStr |
Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
title_full_unstemmed |
Cluster analysis of velocity models around the Hudson Bay region, Eastern Canada |
title_sort |
cluster analysis of velocity models around the hudson bay region, eastern canada |
publisher |
Oxford University Press (OUP) |
publishDate |
2022 |
url |
http://dx.doi.org/10.1093/gji/ggac456 https://academic.oup.com/gji/advance-article-pdf/doi/10.1093/gji/ggac456/47197072/ggac456.pdf https://academic.oup.com/gji/article-pdf/233/1/359/48351688/ggac456.pdf |
genre |
Baffin Island Baffin Hudson Bay |
genre_facet |
Baffin Island Baffin Hudson Bay |
op_source |
Geophysical Journal International volume 233, issue 1, page 359-375 ISSN 0956-540X 1365-246X |
op_rights |
https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model |
op_doi |
https://doi.org/10.1093/gji/ggac456 |
container_title |
Geophysical Journal International |
container_volume |
233 |
container_issue |
1 |
container_start_page |
359 |
op_container_end_page |
375 |
_version_ |
1799477333062582272 |