A Caltech Library Service

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

Quinn, D. P. and Ehlmann, B. L. (2019) A PCA-Based Framework for Determining Remotely Sensed Geological Surface Orientations and Their Statistical Quality. Earth and Space Science, 6 (8). pp. 1378-1408. ISSN 2333-5084. PMCID PMC6853252.

[img] PDF - Published Version
Creative Commons Attribution Non-commercial No Derivatives.

[img] PDF - Submitted Version
Creative Commons GNU LGPL (Software).


Use this Persistent URL to link to this item:


The orientations of planar rock layers are fundamental to our understanding of structural geology and stratigraphy. Remote sensing platforms including satellites, unmanned aerial vehicles, and Light Detection and Ranging scanners are increasingly used to build three-dimensional models of structural features on Earth and other planets. Remotely gathered orientation measurements are straightforward to calculate but subject to uncertainty inherited from input data, differences in viewing geometry, and the plane-fitting process, complicating geological interpretation. Here, we improve upon the present state of the art by developing a generalized means for computing and reporting errors in strike-dip measurements from remotely sensed data. We outline a general framework for representing the error space of uncertain orientations in Cartesian and spherical coordinates and develop a principal component analysis (PCA) regression method, which captures statistical errors independent of viewing geometry and input data structure. We also introduce graphical techniques to visualize the uniqueness and quality of orientation measurements and a process to increase statistical power by jointly fitting bedding planes under the assumption of parallel stratigraphy. These new techniques are validated by comparison of field-gathered orientation measurements with those derived from minimally processed satellite imagery of the San Rafael Swell, Utah, and unmanned aerial vehicle imagery from the Naukluft Mountains, Namibia. We provide software packages supporting planar fitting and visualization of error distributions. This method increases the precision and comparability of structural measurements gathered using a new generation of remote sensing techniques.

Item Type:Article
Related URLs:
URLURL TypeDescription CentralArticle Paper
Quinn, D. P.0000-0003-1895-3742
Ehlmann, B. L.0000-0002-2745-3240
Additional Information:©2019. The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non‐commercial and no modifications or adaptations are made. Received 28 MAY 2018; Accepted 26 MAR 2019; Accepted article online 10 APR 2019; Published online 14 AUG 2019. We would like to thank NASA for the Earth and Space Science Fellowship (NNX14AO61H, to D. P. Quinn) that funded this work. Our software tools are archived at CaltechDATA (Attitude, doi: 10.22002/D1.1211; Orienteer, doi: 10.22002/D1.1212) in conjunction with this work. Data for the examples shown in the paper are part of the testing suite for the Attitude software package.
Group:Astronomy Department
Funding AgencyGrant Number
NASA Earth and Space Science FellowshipNNX14AO61H
Subject Keywords:PCA; structural geology; orientation; unmanned aerial vehicle; photogrammetry; statistics
Issue or Number:8
PubMed Central ID:PMC6853252
Record Number:CaltechAUTHORS:20191202-141235738
Persistent URL:
Official Citation:Quinn, D. P., & Ehlmann, B. L. ( 2019). A PCA‐based framework for determining remotely sensed geological surface orientations and their statistical quality. Earth and Space Science, 6, 1378– 1408.
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:100146
Deposited By: Tony Diaz
Deposited On:04 Dec 2019 18:44
Last Modified:20 Apr 2020 08:47

Repository Staff Only: item control page