Title: Using generative adversarial networks for extraction of InSAR signals from large-scale Sentinel-1 interferograms by improving tropospheric noise correction
Abstract: Spatiotemporal variations of pressure, temperature, water vapour content in the atmosphere lead to significant delays in interferometric synthetic aperture radar (InSAR) measurements of deformations in the ground. One of the key challenges in increasing the accuracy of ground deformation measurements using InSAR is to produce robust estimates of the tropospheric delay. Tropospheric models like ERA-Interim and Global Navigation Satellite System (GNSS) can be used to estimate the total tropospheric delay in interferograms. However, in remote areas or countries with very less GNSS stations, correction by the ERA-Interim model becomes the only choice. The problem with using ERA-Interim model for interferogram correction is that after the tropospheric correction, there are still some residuals left in the interferograms, which can be mainly attributed to turbulent troposphere. to this end, we propose a Generative Adversarial Network (GAN) based approach to mitigate the phase delay caused by troposphere.
Title: Model selection problems and transdimensional inversion methods in geosciences
Description: Classic inversion methods adjust a pre-defined number of parameters to the observed data. In many scenarios, especially interpreting data from imaging methods such as tomography, the parametrization of the inverse model is unknown and thereby biased. By keeping the number of parameters flexible, transdimensional inversion methods eliminate constrains from the initial parametrization and can give more realistic results about the investigated parameter distribution. I will present the potential of this methodology on geothermal and hydrogeological examples.
Access to the zoom meeting: https://zoom.us/j/97891484486