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Application of independent component analysis to multitemporal InSAR data with volcanic case studies
Author(s) -
Ebmeier S. K.
Publication year - 2016
Publication title -
journal of geophysical research: solid earth
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.983
H-Index - 232
eISSN - 2169-9356
pISSN - 2169-9313
DOI - 10.1002/2016jb013765
Subject(s) - interferometric synthetic aperture radar , geology , independent component analysis , remote sensing , volcano , synthetic aperture radar , principal component analysis , independence (probability theory) , geodesy , seismology , computer science , artificial intelligence , statistics , mathematics
A challenge in the analysis of multitemporal interferometric synthetic aperture radar (InSAR) data is distinguishing and separating volcanic, tectonic, and anthropogenic displacements from each other and from atmospheric or orbital noise. Independent component analysis (ICA) is a method for decomposing a mixed signal based on the assumption that the component sources are non‐Gaussian and statistically independent. ICA has potential as a tool for exploratory analysis of InSAR data, and in particular for testing whether geophysical signals are related or independent. This article presents tests of the applicability of ICA to InSAR by using synthetic data and application to Sentinel‐1A archive images from two contrasting examples of volcano deformation. Coeruptive subsidence associated with the April 2015 eruption of Calbuco (Chile) was identified in spatial patterns found by maximizing both spatial and temporal independence. Spatial patterns and rates of lava subsidence were retrieved by using ICA analysis of interferograms from Parícutin lava fields (México) and found to be consistent with previous observations. I demonstrate that ICA is an appropriate method for the analysis of volcanic signals in the presence of atmospheric noise and propose a strategy for the reliable identification of geophysical displacements by using cluster analysis of the spatial patterns of independent components. This approach allows the detection of geophysical processes on a range of scales and provides a test of signal independence where multiple displacement sources are active.