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Geomonitoring as a contribution to process understanding of river

The inversion of gravity data for crustal thicknesses is a nonunique problem. Therefore, additional independent information (e.g., seismic data) is needed to constrain the inversion process. Despite decades of exploration efforts related to mining and the installation of more seismic stations, knowledge on the deep crustal structure of southern Africa remains limited. In this contribution we present a crustal thickness model for southern Africa: The initial model is determined by inversion of satellite gravity data. Here, we apply seismically constrained non-linear inversion, based on the modified Bott's method and Tikhonov regularization assuming spherical Earth approximation. The inversion hyper-parameters are determined by Monte-Carlo-Marcov-Chain (MCMC) simulation. The data quality of the (active and passive) seismic constraints is high in general, showing e.g. individual uncertainties per point. The problem is that the constraining data points are irregularly distributed, resulting in large areas without constraints. Therefore, in a next step, we want to validate and improve the modelling result for these unconstrained regions. We use the initial constraining data set to geostatistically simulate a homogeneous crustal thickness model for the investigation region. For this, we apply a Sequential Gaussian Simulation (SGS) based on Ordinary Kriging that includes the uncertainties of the seismic data and allows to characterize uncertainties of the simulated points. The simulated crustal thickness model is then used to qualitatively validate the inversion result. Additionally, we redo the inversion process with a new constraining data set that combines the preexisting constraining points and the simulated model.


Peter Menzel1, Mohamed Sobh1, Islam Fadel2, Christian Gerhards1
1Technische Universität Bergakademie Freiberg, Germany; 2University of Twente, Enschede, Netherlands
GeoKarlsruhe 2021
South Africa