ALBERT

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: Hyperspectral airborne campaigns have been carried out in the frame of the data exploitation and application development program of the German Environmental Mapping and Analysis Program (EnMAP) to support method and application development in the prelaunch phase of the EnMAP satellite mission. A metadata portal (EnMAP Campaign Portal) has been set up providing general information about the campaigns, recorded airborne hyperspectral data sets, other data associated to the respective campaigns like field and laboratory measurements and a number of field guides for in-situ data acquisition. Furthermore, it informs about the availability of simulated EnMAP and Sentinel-2 data for the respective campaign region. The data listed in the EnMAP Campaign Portal is freely available under a Creative Commons License as DOI-referenced data publications.

Description: For the visualization and analysis of the stress field from 3D thermo-hydro-mechanical (THM) numerical model results two main technical steps are necessary. First, one has to derive from the six independent components of the 3D stress tensor scalar and vector values such as the orientation and magnitude of the maximum and minimum horizontal stress, stress ratios, or the differential stress. It is also of great interest to display e.g. the normal and shear stress with respect to an arbitrarily given surface. Second, an appropriate geometry should be given such as cross sections, profile e.g. for borehole pathways or surfaces on which the model results and further derived values are interpolated. This includes also the three field variables temperature, pore pressure and the displacement vector. To facilitate and automate these steps the Add-on GeoStress for the professional visualization software Tecplot 360 EX has been programmed. Besides the aforementioned values derived from the stress tensor the tool also allows to calculate the values of Coulomb Failure Stress (CFS), Slip and Dilation tendency (ST and DT) and Fracture Potential (FP). GeoStress also estimates kinematic variables such as horizontal slip, dip slip, rake vector of faults that are implemented as contact surfaces in the geomechanical-numerical model as well as the true vertical depth (TVD). Furthermore, the Add-on can import surface and polyline geometries and interpolates on these all available stress parameter. This technical report describes the visualization tool with examples using 3D geomechanical-numerical model results from the finite element software Abaqus v2019. It also presents a number of special features of Tecplot 360 EX in combination with GeoStress that allow a professional and efficient analysis. We also address now the usage GeoStress with PyTecplot which is a powerful tool to automize the analysis. The Add-on as well as the example and input files used in this manual is published by Stromeyer et al. (2020) and the table below gives an overview of the files with a short explanation as they appear in the manual.

Description: The distribution of data records for the maximum horizontal stress orientation SHmax in the Earth’s crust is sparse and very unequally. To analyse the stress pattern and its wavelength and to predict the mean SHmax 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 SHmax 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 SHmax orientation on regular grids. The first is using a fixed search radius around the grid points and computes the mean SHmax 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 SHmax orientation is below a given threshold. This approach delivers mean SHmax 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 SHmax orientation.

Description: The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures. The script files are provided for download at http://github.com/MorZieg/FAST_Calibration. Tab. 0-1 gives an overview of the folder structure and input files with a short explanation.

Description: Hyperspectral airborne campaigns have been carried out in the frame of the data exploitation and application development program of the German Environmental Mapping and Analysis Program (EnMAP) to support method and application development in the prelaunch phase of the EnMAP satellite mission. A metadata portal (EnMAP Campaign Portal) has been set up providing general information about the campaigns, recorded airborne hyperspectral data sets, other data associated to the respective campaigns like field and laboratory measurements and a number of field guides for in-situ data acquisition. Furthermore, it informs about the availability of simulated EnMAP and Sentinel-2 data for the respective campaign region. The data listed in the EnMAP Campaign Portal is freely available under a Creative Commons License as DOI-referenced data publications.

Description: Earthquake focal mechanism solutions (FMS) form the basic data input for many applications, e.g. stress tensor inversion or ground-motion prediction equation estimation. In these applications the FMS data is usually binned spatially or in predetermined ranges of rake and dip based on expert elicitation. However, due to the significant increase of FMS data in the past decade an objective data-driven cluster analysis is now possible. Here we present the method ACE (Angular Classification with Expectation-Maximization) that identities clusters of FMS without a priori information. The identified clusters can be used for the classification of the Style-of- Faulting and as weights for FMS data binning in the aforementioned applications. As an application example we use ACE to identify FMS clusters according to their Style-of- Faulting that are related to certain earthquake types (e.g. subduction interface) in northern Chile, the Nazca Plate and in Kyushu (Japan). We use the resulting clusters and weights as a priori information for a stress tensor inversion for these regions and show that uncertainties of the stress tensor estimates are reduced significantly.

Description: The 3D geomechanical-numerical modelling aims at a continuous description of the stress state in a subsurface volume. The model is fitted to the model-independent stress data records by adaptation of the displacement boundary conditions. This process is herein referred to as model calibration. Depending on the amount of available stress data records and the complexity of the model the calibration can be a lengthy process of trial-and-error to estimate the best-fit boundary conditions. The tool FAST Calibration (Fast Automatic Stress Tensor Calibration) is a Matlab script that facilitates and speeds up this calibration process. By using a linear regression it requires only three test model scenarios with different displacement boundary conditions to calibrate a geomechanical-numerical model on available stress data records. The differences between the modelled and observed stresses are used for the linear regression that allows to compute the displacement boundary conditions required for the best-fit estimation. The influence of observed stress data records on the best-fit displacement boundary conditions can be weighted. Furthermore, FAST Calibration provides a cross checking of the best-fit estimate against indirect stress information that cannot be used for the calibration process, such as the observation of borehole breakouts or drilling induced fractures. In order to bridge the scale gap between a regional stress model and a local reservoir model, the multistage calibration procedure is applied where a local model is calibrated solely on the stress state provided by a regional model. FAST Calibration provides the necessary tools and guidelines. The script files are provided for download at http://github.com/MorZieg/FAST_Calibration. Tab. 0-1 gives an overview of the folder structure and input files with a short explanation.