Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland

Inverse magnetotelluric (MT) problems are non-unique and smoothing criteria are typically added to choose the "best" model. However, the process often produces an unrealistic geological model. In reality the subsurface geology is differentiated by distinct rock units that are often better...

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Published in:Proceedings, Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining
Main Authors: Kieu, D., Kepic, Anton, Le, V.
Format: Conference Object
Language:unknown
Published: 2016
Subjects:
Online Access:https://hdl.handle.net/20.500.11937/31196
https://doi.org/10.3997/2214-4609.201602132
id ftcurtin:oai:espace.curtin.edu.au:20.500.11937/31196
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spelling ftcurtin:oai:espace.curtin.edu.au:20.500.11937/31196 2023-06-11T04:15:19+02:00 Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland Kieu, D. Kepic, Anton Le, V. 2016 restricted https://hdl.handle.net/20.500.11937/31196 https://doi.org/10.3997/2214-4609.201602132 unknown http://hdl.handle.net/20.500.11937/31196 doi:10.3997/2214-4609.201602132 Conference Paper 2016 ftcurtin https://doi.org/20.500.11937/3119610.3997/2214-4609.201602132 2023-05-30T19:36:26Z Inverse magnetotelluric (MT) problems are non-unique and smoothing criteria are typically added to choose the "best" model. However, the process often produces an unrealistic geological model. In reality the subsurface geology is differentiated by distinct rock units that are often better defined by boundaries rather diffuse or smooth boundaries. We present the application of fuzzy clustering as an added constraint within the inversion process to guide model updates toward earth models that are "blocky", and thus resemble geological units. Fuzzy clustering divides the simulated model into clusters based on the similarity of model features. Moreover, fuzzy clustering naturally enables the inclusion of additional prior information in the inversion process, such as petrophysical information from borehole data. The inclusion of this information produces geo-electrical distributions that are more representative of the true rock units. This is demonstrated through the case study of the Kevitsa Ni-Cu-PGE deposit, northern Finland. The inversion can detect the ore zones and carbonaceous phyllite relating to the conductive zones. The inverted cluster generated model is compare better with borehole data than other approaches. Conference Object Northern Finland Curtin University: espace Proceedings, Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining
institution Open Polar
collection Curtin University: espace
op_collection_id ftcurtin
language unknown
description Inverse magnetotelluric (MT) problems are non-unique and smoothing criteria are typically added to choose the "best" model. However, the process often produces an unrealistic geological model. In reality the subsurface geology is differentiated by distinct rock units that are often better defined by boundaries rather diffuse or smooth boundaries. We present the application of fuzzy clustering as an added constraint within the inversion process to guide model updates toward earth models that are "blocky", and thus resemble geological units. Fuzzy clustering divides the simulated model into clusters based on the similarity of model features. Moreover, fuzzy clustering naturally enables the inclusion of additional prior information in the inversion process, such as petrophysical information from borehole data. The inclusion of this information produces geo-electrical distributions that are more representative of the true rock units. This is demonstrated through the case study of the Kevitsa Ni-Cu-PGE deposit, northern Finland. The inversion can detect the ore zones and carbonaceous phyllite relating to the conductive zones. The inverted cluster generated model is compare better with borehole data than other approaches.
format Conference Object
author Kieu, D.
Kepic, Anton
Le, V.
spellingShingle Kieu, D.
Kepic, Anton
Le, V.
Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
author_facet Kieu, D.
Kepic, Anton
Le, V.
author_sort Kieu, D.
title Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
title_short Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
title_full Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
title_fullStr Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
title_full_unstemmed Fuzzy clustering constrained magnetelluric inversion - Case study over the Kevitsa ultramafic intusion, Northern Finland
title_sort fuzzy clustering constrained magnetelluric inversion - case study over the kevitsa ultramafic intusion, northern finland
publishDate 2016
url https://hdl.handle.net/20.500.11937/31196
https://doi.org/10.3997/2214-4609.201602132
genre Northern Finland
genre_facet Northern Finland
op_relation http://hdl.handle.net/20.500.11937/31196
doi:10.3997/2214-4609.201602132
op_doi https://doi.org/20.500.11937/3119610.3997/2214-4609.201602132
container_title Proceedings, Near Surface Geoscience 2016 - First Conference on Geophysics for Mineral Exploration and Mining
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