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A PCA-Based Framework for Determining Remotely Sensed Geological Surface Orientations and Their Statistical Quality.

D P Quinn1, B L Ehlmann1,2

  • 1Division of Geological and Planetary Sciences California Institute of Technology Pasadena CA USA.

Earth and Space Science (Hoboken, N.J.)
|November 26, 2019
PubMed
Summary

This study introduces a new method for calculating and reporting errors in geological strike-dip measurements from remote sensing data. The approach enhances the precision and comparability of structural geology data derived from satellites and drones.

Keywords:
PCAorientationphotogrammetrystatisticsstructural geologyunmanned aerial vehicle

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Area of Science:

  • Geosciences
  • Structural Geology
  • Remote Sensing

Background:

  • Planar rock layer orientations are crucial for structural geology and stratigraphy.
  • Remote sensing (satellites, UAVs, LiDAR) is increasingly used for 3D geological modeling.
  • Orientation measurements from remote sensing have inherent uncertainties complicating interpretation.

Purpose of the Study:

  • To develop a generalized method for computing and reporting errors in strike-dip measurements from remotely sensed data.
  • To improve the precision and comparability of structural measurements obtained via remote sensing.

Main Methods:

  • Developed a framework for representing orientation error in Cartesian and spherical coordinates.
  • Applied principal component analysis (PCA) regression for error analysis independent of viewing geometry.
  • Introduced graphical techniques for visualizing measurement quality and uniqueness.
  • Implemented joint fitting of bedding planes for increased statistical power.

Main Results:

  • Validated the new techniques using field data and remote sensing imagery from Utah and Namibia.
  • Demonstrated improved precision and comparability of strike-dip measurements.
  • Provided software for planar fitting and error distribution visualization.

Conclusions:

  • The developed method offers a robust approach to quantifying uncertainties in remotely sensed geological orientation data.
  • This advancement facilitates more reliable geological interpretations and structural analysis.
  • The techniques support the effective use of next-generation remote sensing technologies in Earth and planetary science.