The Tornquist Zone, a north east inclining lithospheric transition at the south western margin of the Baltic Shield: Revealed through a nonlinear teleseismic tomographic inversion

From July 1996 to August 1997 the TOR project operated 130 seismographs in North Germany, Denmark and South Sweden, with the aim of collecting signals from local, regional and teleseismic earthquakes. This data set is particularly interesting since the seismic antenna crosses the most significant geological boundary in Europe, the Tornquist Zone, which in the northern part is the border between the Baltic Shield and the younger European lithosphere. Previous studies have shown significant physical changes in the crust and upper mantle across this transition zone, including two independent teleseismic tomographic studies of the TOR data set. But these two studies disagree on the orientation of the slope of the transition. Both studies used an iterative linearized inversion method. We will in this work Preprint submitted to Elsevier Science 27 July 2005 present an inversion based on Bayesian statistics, where the solution space is examined in order to study a very large number of tomographic solutions and to examine the solution uniqueness and uncertainty. The method is applied to measurements of 3345 relative teleseismic P-phase travel times from 48 teleseismic earthquakes with good azimuthal coverage with respect to the great circle arc of the TOR array. We find the lithospheric transition to be a north east inclination of around 30° to 45° off vertical.     

Application of Bayesian Generative Adversarial Networks to Geological Facies Modeling

Mathematical Geosciences - Geological facies modeling is a key component in exploration and characterization of subsurface reservoirs. While traditional geostatistical approaches are still commonly...     

A Frequency Matching Method: Solving Inverse Problems by Use of Geologically Realistic Prior Information

The frequency matching method defines a closed form expression for a complex prior that quantifies the higher order statistics of a proposed solution model to an inverse problem. While existing solution methods to inverse problems are capable of sampling the solution space while taking into account arbitrarily complex a priori information defined by sample algorithms, it is not possible to directly compute the maximum a posteriori model, as the prior probability of a solution model cannot be expressed. We demonstrate how the frequency matching method enables us to compute the maximum a posteriori solution model to an inverse problem by using a priori information based on multiple point statistics learned from training images. We demonstrate the applicability of the suggested method on a synthetic tomographic crosshole inverse problem.