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2017•Publication Database GFZ (GFZ German Research Centre for Geosciences)Open access

Source Model For The Copahue Volcano Magma Plumbing System Constrained By Insar Surface Deformation Observations

P. Lundgren, Mehdi Nikkhoo, Sergey Samsonov, Pietro Milillo, Fernando Gil‐Cruz, Jonathan Lazo

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Abstract

Tar files for each of the InSAR time series (interferograms used in the GIAnT time series computation as well as the input files and outputs from using GIAnT). GIAnT is an open source InSAR time series code developed at Caltech. The UAVSAR_*.tgz files contain the interferograms from the UAVSAR airborne system that were used in the analysis. The actual model input files require some additional down sampling using resamptool.m a Matlab code developed by Prof. R. Lohman, Cornell Univ.

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What this paper is about

Tar files for each of the InSAR time series (interferograms used in the GIAnT time series computation as well as the input files and outputs from using GIAnT). GIAnT is an open source InSAR time series code developed at Caltech. The UAVSAR_*.tgz files contain the interferograms from the UAVSAR airborne system that were used in the analysis. The actual model input files require some additional down sampling using resamptool.m a Matlab code developed by Prof. R. Lohman, Cornell Univ.

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Available abstract

Tar files for each of the InSAR time series (interferograms used in the GIAnT time series computation as well as the input files and outputs from using GIAnT). GIAnT is an open source InSAR time series code developed at Caltech. The UAVSAR_*.tgz files contain the interferograms from the UAVSAR airborne system that were used in the analysis. The actual model input files require some additional down sampling using resamptool.m a Matlab code developed by Prof. R. Lohman, Cornell Univ.

Key concepts: Geology, Volcano, Deformation (meteorology), Geodesy, Seismology, Oceanography

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Source Model For The Copahue Volcano Magma Plumbing System Constrained By Insar Surface Deformation Observations — Research Paper | ScholarLens