ALBERT

Description: The human brain is able to integrate a myriad of information in an enormous and massively parallel network of neurons that are divided into functionally specialized regions such as the visual cortex, auditory cortex, or dorsolateral prefrontal cortex. Each of these regions participates as a context-dependent, self-organized, and transient subnetwork, which is shifted by changes in attention every 0.5 to 2 s. This leads to one of the most puzzling issues in cognitive neuroscience, well known as the "binding problem." The concept of neural synchronization tries to explain the problem by encoding information using coherent states, which temporally patterns neural activity. We show that memristive devices, that is, a two-terminal variable resistor that changes its resistance depending on the previous charge flow, allow a new degree of freedom for this concept: a local memory that supports transient connectivity patterns in oscillator networks. On the basis of the probability distribution of the resistance switching process of Ag-doped titanium dioxide memristive devices, a local plasticity model is proposed, which causes an autonomous phase and frequency locking in an oscillator network. To illustrate the performance of the proposed computing paradigm, the temporal binding problem is investigated in a network of memristively coupled self-sustained van der Pol oscillators. We show evidence that the implemented network allows achievement of the transition from asynchronous to multiple synchronous states, which opens a new pathway toward the construction of cognitive electronics.

Description: Changes in the formation of dense water in the Arctic Ocean and Nordic Seas [the “Arctic Mediterranean” (AM)] probably contributed to the altered climate of the last glacial period. We examined past changes in AM circulation by reconstructing radiocarbon ventilation ages of the deep Nordic Seas over the past 30,000 years. Our results show that the glacial deep AM was extremely poorly ventilated (ventilation ages of up to 10,000 years). Subsequent episodic overflow of aged water into the mid-depth North Atlantic occurred during deglaciation. Proxy data also suggest that the deep glacial AM was ~2° to 3°C warmer than modern temperatures; deglacial mixing of the deep AM with the upper ocean thus potentially contributed to the melting of sea ice, icebergs, and terminal ice-sheet margins.

Description: In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. In particular, low angle outcropping or out-pinching geological units, i.e. geological layers that are represented in the model volume, pose serious challenges. Another example are changes in the geometry of a model, which can occur at one point of a project, when re-meshing is not an option anymore or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the element location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon. The script files are provided for download at http://github.com/MorZieg/APPLE_PY. Table 0-1 gives an overview of the folder structure and input files with a short explanation.

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: In geosciences 3D geomechanical-numerical models are used to estimate the in-situ stress state. In such a model each geological unit is populated with the rock properties Young’s module, Poisson ratio, and density. Usually, each unit is assigned a single set of homogeneous properties. However, variable rock properties are observed and expected within the same geological unit. Even in small volumes large variabilities may. The Python script HIPSTER (Homogeneous to Inhomogeneous rock Properties for Stress TEnsor Research) provides an algorithm to include inhomogeneities in geomechanical-numerical models that use the solver Abaqus®. The user specifies the mean values for the rock properties Young's module, Poisson ratio and density, and their variability for each geological unit. The variability of the material properties is individually defined for each of the three rock properties in each geological layer. For each unit HIPSTER generates a normal or uniform distribution for each rock property. From these distri-butions for each single element HIPSTER draws individual rock properties and writes them to a separate material file. This file defines different material properties for each element. The file is included in the geomechanical-numerical analysis solver deck and the numerical model is solved as usual.

Description: The 3D geomechanical-numerical modelling of the in-situ stress state requires observed stress information at reference locations within the model area to be compared to the modelled stress state. This comparison of stress states and the ensuing adaptation of the displacement boundary conditions provide a best fit stress state in the entire model region that is based on the available stress information. This process is also referred to as calibration. Depending on the amount of available information and the complexity of the model the calibration is a lengthy process of trial-and-error modelling and analysis. The Fast Automatic Stress Tensor Calibration (FAST Calibration) is a method and a Matlab script that facilitates and speeds up the calibration process. The method requires only three model scenarios with different boundary conditions. The modelled stress states at the locations of the observed stress state are extracted. Then they are used to compute the displacement boundary conditions that are required in order to achieve the best fit of the modelled to the observed stress state. Furthermore, the influence of the individual observed stress information on the resulting stress state can be weighted.

Description: In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. Es-pecially outcropping or out-pinching geological units, i.e. geological layers that are rep-resented in the model volume, pose serious challenges. Changes in the geometry of a model may occur well into a project at a point, when re-meshing is not an option any-more or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing uniform mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and contains no or only very few and simple structures. The mesh has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the ele-ment location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon.

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.