Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data


Graph theory is useful for estimating time-dependent model parameters via weighted least-squares using interferometric synthetic aperture radar (InSAR) data. Plotting acquisition dates (epochs) as vertices and pair-wise interferometric combinations as edges defines an incidence graph. The edge-vertex incidence matrix and the normalized edge Laplacian matrix are factors in the covariance matrix for the pair-wise data. Using empirical measures of residual scatter in the pair-wise observations, we estimate the variance at each epoch by inverting the covariance of the pair-wise data. We evaluate the rank deficiency of the corresponding least-squares problem via the edge-vertex incidence matrix. We implement our method in a MATLAB software package called GraphTreeTA available on GitHub ( We apply temporal adjustment to the data set described in Lu et al. (2005) at Okmok volcano, Alaska, which erupted most recently in 1997 and 2008. The data set contains 44 differential volumetric changes and uncertainties estimated from interferograms between 1997 and 2004. Estimates show that approximately half of the magma volume lost during the 1997 eruption was recovered by the summer of 2003. Between June 2002 and September 2003, the estimated rate of volumetric increase is (6.2 +/- 0.6) x 10^6 m^3/yr. Our preferred model provides a reasonable fit that is compatible with viscoelastic relaxation in the five years following the 1997 eruption. Although we demonstrate the approach using volumetric rates of change, our formulation in terms of incidence graphs applies to any quantity derived from pair-wise differences, such as wrapped phase or wrapped residuals.

Date of final oral examination: 05/19/2016
This thesis is approved by the following members of the Final Oral Committee: Kurt L. Feigl, Professor, Geoscience
Michael Cardiff, Assistant Professor, Geoscience
Clifford H. Thurber, Vilas Distinguished Professor, Geoscience

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DOE Project Name: PoroTomo Project
DOE Project Number: EE0006760
DOE Project Lead: Elisabet Metcalfe
Last Updated: 12 months ago
Data from July, 2016
Submitted Jul 13, 2018


University of Wisconsin



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Elena Reinisch
University of Wisconsin


energy, PoroTomo, InSAR, time series, temporal adjustment, graph theory, poroelastic tomography, thesis, paper, model, parameters, modeling, time-dependent, time-varying, weighted least-squares, inversion, radar, interferometric, synthetic aperture, laplacian, covariance, matrix, MatLab, GraphTreeTA, Alaska, AK, Okmok, volcano, magma, volume, method, implementation, application, viscous, flow, viscoelastic relaxation, remote sensing


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