ALBERT

Description: In Northwest Anatolia, the dextral North Anatolian Fault Zone (NAFZ) goes through the Sea of Marmara and cre-ates a section which is known as the Main Marmara Fault (MMF). Due to the NAFZ activity, the Marmara regionis a major earthquake zone. This area hosts the Megacity of Istanbul in the vicinity of a seismic gap (∼150 kmlong) in the MMF which has not ruptured since 1766. There is an ongoing controversial debate regarding the causeof the seismic gap and if either the fault is locked to a certain depth or is creeping. The main question is if the faultis geomechanically segmented or if the energy will be released over a big single rupture surface. To contribute tothis discussion a detailed description and understanding of the lithosphere thermomechanical behaviour below theSea of Marmara is key. In this study, we present 3D lithospheric-scale thermal and rheological models of the Sea ofMarmara. These models are based on a 3D density model which is obtained from geological and geophysical dataintegration and constrained by gravity modelling. Accordingly, the lithosphere structure consists of six major lay-ers. Two layers of syn- and pre-kinematic sediments with respect to the Sea of Marmara formation with an averagedensity (ρ) of 2000 and 2490 kg.m−3, respectively. These sediments rest on a heterogeneous crust including a felsicupper crystalline crust (ρ= 2720 kg.m−3)and an intermediate to mafic lower crystalline crust (ρ= 2890 kg.m−3).The crystalline crustal units are crosscut by two thick dome-shaped mafic high-density bodies (ρ= 3050 kg.m−3),that spatially correlate with the bending segments of the MMF. Beneath these layers is a homogeneous lithosphericmantle (ρ= 3300 kg.m−3)down to the thermal Lithosphere-Asthenosphere boundary (LAB). Along the MMF,the thermomechanical model generally indicates that the brittle-ductile transition zone occurs within the uppercrystalline crust at a depth of around 18 km b.s.l, which is consistent with the 1999 Izmit earthquake. In contrast,the thermomechanical model indicates that the high-density bodies are colder and stronger than the surroundingcrystalline units. Consequently, the brittle-ductile transition zone occurs, closer to the Moho discontinuity, at thedepth around 23 km b.s.l. In conclusion, these results suggest that crustal heterogeneities significantly affect therheological behaviour of the MMF, and support the hypothesis that the fault is geomechanically segmented.

Description: Dataset

Description: These data are supplementary material to Ziegler & Heidbach (2020) and present the results of a 3D geomechanical-numerical model of the stress state with quantified uncertainties. The average modelled stress state is provided for each of the six components of the full stress tensor. In addition, the associated standard deviation for each component is provided. The modelling approach uses a published lithological model and the used data is described in the publication Ziegler & Heidbach (2020). The reduced stress tensor is derived using the Tecplot Addon GeoStress (Stromeyer & Heidbach, 2017).The model results are provided in a comma-separated ascii file. Each line in the file represents one of the approx. 3 million finite elements that comprise the model.

Description: The distribution of data records for the maximum horizontal stress orientation S_Hmax in the Earth’s crust is sparse and very unequally. To analyse the stress pattern and its wavelength and to predict the mean S_Hmax orientation on regular grids, statistical interpolation as conducted e.g. by Coblentz and Richardson (1995), Müller et al. (2003), Heidbach and Höhne (2008), Heidbach et al. (2010) or Reiter et al. (2014) is necessary. Based on their work we wrote the Matlab® script Stress2Grid that provides several features to analyse the mean S_Hmax pattern. The script facilitates and speeds up this analysis and extends the functionality compared to the publications mentioned before. This script is the update of Stress2Grid v1.0 (Ziegler and Heidbach, 2017). It provides two different concepts to calculate the mean S_Hmax orientation on regular grids. The first is using a fixed search radius around the grid points and computes the mean S_Hmax orientation if sufficient data records are within the search radius. The larger the search radius the larger is the filtered wavelength of the stress pattern. The second approach is using variable search radii and determines the search radius for which the standard deviation of the mean S_Hmax orientation is below a given threshold. This approach delivers mean S_Hmax orientations with a user-defined degree of reliability. It resolves local stress perturbations and is not available in areas with conflicting information that result in a large standard deviation. Furthermore, the script can also estimate the deviation between plate motion direction and the mean S_Hmax orientation. The script is fully documented by the accompanying WSM Technical Report 19/02 (Ziegler and Heidbach, 2019) which includes a changelog in the beginning.

Description: The 3D geomechanical-numerical modelling of the in-situ stress state aims at a continuous description of the stress state in a subsurface volume. It requires observed stress information within the model volume that are used as a reference. Once the modelled stress state is in agreement with the observed reference stress data the model is assumed to provide the continuous stress state in its entire volume. The modelled stress state is fitted to the reference stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as calibration. Depending on the amount of available stress data records and the complexity of the model the manual calibration is a lengthy process of trial-and-error modelling and analysis until best-fit boundary conditions are found. The Fast Automatic Stress Tensor Calibration (FAST Calibration) is a Python function that facilitates and speeds up this calibration process. By using a linear regression it requires only three model scenarios with different boundary conditions. The stress states from the three model scenarios at the locations of the reference stress data records are extracted. The differences between the modelled and observed stress states are used for a linear regression that allows to compute the displacement boundary conditions required for the best-fit modelled stress state. If more than one reference stress state is provided, the influence of the individual observed stress data records on the best-fit boundary conditions can be weighted. The script files are provided for download at: http://github.com/MorZieg/PyFAST_Calibration

Description: This thesis summarizes the results of the WSM project’s second phase (1996‐2008). In particular it presents the major achievements that have been accomplished with the WSM 2008 database release that has been compiled under the guidance of the author. Furthermore, the thesis briefly presents three of the author’s numerical models that aim at quantification the temporal changes of the crustal stress field.

Description: Postseismic surface deformation associated with great subduction earthquakes is controlled by asthenosphere rheology, frictional properties of the fault, and structural complexity. Here by modeling GPS displacements in the 6 years following the 2010 Mw 8.8 Maule earthquake in Chile, we investigate the impact of heterogeneous viscosity distribution in the South American subcontinental asthenosphere on the 3-D postseismic deformation pattern. The observed postseismic deformation is characterized by flexure of the South America plate with peak uplift in the Andean mountain range and subsidence in the hinterland. We find that, at the time scale of observation, over 2 orders of magnitude gradual increase in asthenosphere viscosity from the arc area toward the cratonic hinterland is needed to jointly explain horizontal and vertical displacements. Our findings present an efficient method to estimate spatial variations of viscosity, which clearly improves the fitting to the vertical signal of deformation. Lateral changes in asthenosphere viscosity can be correlated with the thermomechanical transition from weak subvolcanic arc mantle to strong subcratonic mantle, thus suggesting a stationary heterogeneous viscosity structure. However, we cannot rule out a transient viscosity structure (e.g., power law rheology) with the short time span of observation.