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

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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 (https://github.com/feigl/gipht). 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

Citation Formats

TY - DATA AB - 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 (https://github.com/feigl/gipht). 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 AU - Reinisch, Elena DB - Open Energy Data Initiative (OEDI) DP - Open EI | National Renewable Energy Laboratory DO - KW - energy KW - PoroTomo KW - InSAR KW - time series KW - temporal adjustment KW - graph theory KW - poroelastic tomography KW - thesis KW - paper KW - model KW - parameters KW - modeling KW - time-dependent KW - time-varying KW - weighted least-squares KW - inversion KW - radar KW - interferometric KW - synthetic aperture KW - laplacian KW - covariance KW - matrix KW - MatLab KW - GraphTreeTA KW - Alaska KW - AK KW - Okmok KW - volcano KW - magma KW - volume KW - method KW - implementation KW - application KW - viscous KW - flow KW - viscoelastic relaxation KW - remote sensing LA - English DA - 2016/07/28 PY - 2016 PB - University of Wisconsin T1 - Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data UR - https://data.openei.org/submissions/7226 ER -
Export Citation to RIS
Reinisch, Elena. Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data. University of Wisconsin, 28 July, 2016, GDR. https://gdr.openei.org/submissions/1075.
Reinisch, E. (2016). Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data. [Data set]. GDR. University of Wisconsin. https://gdr.openei.org/submissions/1075
Reinisch, Elena. Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data. University of Wisconsin, July, 28, 2016. Distributed by GDR. https://gdr.openei.org/submissions/1075
@misc{OEDI_Dataset_7226, title = {Graph Theory for Analyzing Pair-wise Data: Application to Interferometric Synthetic Aperture Radar Data}, author = {Reinisch, Elena}, abstractNote = {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 (https://github.com/feigl/gipht). 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}, url = {https://gdr.openei.org/submissions/1075}, year = {2016}, howpublished = {GDR, University of Wisconsin, https://gdr.openei.org/submissions/1075}, note = {Accessed: 2025-04-24} }

Details

Data from Jul 28, 2016

Last updated Jan 27, 2020

Submitted Jul 13, 2018

Organization

University of Wisconsin

Contact

Kurt Feigl

608.262.8960

Authors

Elena Reinisch

University of Wisconsin

Research Areas

DOE Project Details

Project Name PoroTomo Project

Project Lead Elisabet Metcalfe

Project Number EE0006760

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