Article

 

Robust principal component analysis of electromagnetic arrays with missing data Public Deposited

Downloadable Content

Download PDF
https://ir.library.oregonstate.edu/concern/articles/tt44ps31n

Descriptions

Attribute NameValues
Creator
Abstract
  • We describe a new algorithm for robust principal component analysis (PCA) of electromagnetic (EM) array data, extending previously developed multivariate methods to include arrays with large data gaps, and only partial overlap between site occupations. Our approach is based on a criss-cross regression scheme in which polarization parameters and spatial modes are alternately estimated with robust regression procedures. The basic scheme can be viewed as an expectation robust (ER) algorithm, of the sort that has been widely discussed in the statistical literature in the context of robust PCA, but with details of the scheme tailored to the physical specifics of EM array observations. We have tested our algorithm with synthetic and real data, including data denial experiments where we have created artificial gaps, and compared results obtained with full and incomplete data arrays. These tests reveal that for modest amounts of missing data (up to 20 per cent or so) the algorithm performs well, reproducing essentially the same dominant spatial modes that would be obtained from analysis of the complete array. The algorithm thus makes multivariate analysis practical for the first time for large heterogeneous arrays, as we illustrate by application to two different EM arrays.
  • Keywords: Time series analysis, Geomagnetic induction, Magnetotelluric
Resource Type
DOI
Date Available
Date Issued
Citation
  • Smirnov, M. Yu. and Egbert, G. D. (2012), Robust principal component analysis of electromagnetic arrays with missing data. Geophysical Journal International, 190: 1423–1438. doi: 10.1111/j.1365-246X.2012.05569.x
Journal Title
Journal Volume
  • 190
Journal Issue/Number
  • 3
Academic Affiliation
Rights Statement
Funding Statement (additional comments about funding)
  • This work was partially supported by grants from NASA (NNX08AG04G) and NSF (EAR-0739111) to GDE, and by Academy of Finland (136345) to MYuS.
Publisher
Peer Reviewed
Language
Replaces

Relationships

Parents:

This work has no parents.

Items