Extraction of high-resolution structural orientations from digital data: A Bayesian approach

Research output: Contribution to journalArticleResearchpeer-review

Abstract

Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fitting that can use data extracted from digital outcrop models to constrain the orientation of structures and the associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain). The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.

Original languageEnglish
Pages (from-to)106-115
Number of pages10
JournalJournal of Structural Geology
Volume122
DOIs
Publication statusPublished - 1 May 2019

Keywords

  • Digital outcrop geology
  • Plane-fitting
  • Orientation measurement
  • Structure normal estimate
  • Uncertainty

Cite this

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title = "Extraction of high-resolution structural orientations from digital data: A Bayesian approach",
abstract = "Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fitting that can use data extracted from digital outcrop models to constrain the orientation of structures and the associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain). The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.",
keywords = "Digital outcrop geology, Plane-fitting, Orientation measurement, Structure normal estimate, Uncertainty",
author = "Thiele, {Samuel Thomas} and Grose, {Lachlan James} and Tiangang Cui and Steven Micklethwaite and Cruden, {Alexander Ramsay}",
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doi = "10.1016/j.jsg.2019.03.001",
language = "English",
volume = "122",
pages = "106--115",
journal = "Journal of Structural Geology",
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Extraction of high-resolution structural orientations from digital data : A Bayesian approach. / Thiele, Samuel Thomas; Grose, Lachlan James; Cui, Tiangang; Micklethwaite, Steven; Cruden, Alexander Ramsay.

In: Journal of Structural Geology, Vol. 122, 01.05.2019, p. 106-115.

Research output: Contribution to journalArticleResearchpeer-review

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T2 - A Bayesian approach

AU - Thiele, Samuel Thomas

AU - Grose, Lachlan James

AU - Cui, Tiangang

AU - Micklethwaite, Steven

AU - Cruden, Alexander Ramsay

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N2 - Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fitting that can use data extracted from digital outcrop models to constrain the orientation of structures and the associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain). The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.

AB - Measurement of structure orientations is a key part of structural geology. Digital outcrop methods provide a unique opportunity to collect such measurements in unprecedented numbers, and are becoming widely applied. However, orientation estimates produced by plane fitting can be highly uncertain, especially when observed data are approximately collinear or the structures of interest comprise differently oriented segments. Here we present a Bayesian approach to plane fitting that can use data extracted from digital outcrop models to constrain the orientation of structures and the associated uncertainty. We also describe a moving-window search algorithm that exploits this Bayesian formulation to estimate local structure orientations for segmented structures. These methods are validated on synthetic datasets for which both the structure orientation and associated uncertainty is known. Finally, we implement the method in the point cloud analysis package CloudCompare and use it to estimate the orientation and thickness of dykes exposed in cliffs on the island of La Palma (Spain). The results highlight the potential of this method to generate structural data at unprecedented spatial resolution, while simultaneously characterising the associated uncertainties.

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KW - Structure normal estimate

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