The World Stress Map (WSM) is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ Helmholtz Centre for Geosciences. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale. The stress map displays A-C quality stress data records of the Earth’s crust from the WSM database release 2025 (doi:10.5880/WSM.2025.001). Further detailed information on the WSM quality ranking scheme 2025, guidelines for the borehole logging data, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org.
The World Stress Map (WSM) is a global compilation of information on the crustal present-day stress field. It is a collaborative project between academia and industry that aims to characterize the stress pattern and to understand the stress sources. It commenced in 1986 as a project of the International Lithosphere Program under the leadership of Mary-Lou Zoback. From 1995-2008 it was a project of the Heidelberg Academy of Sciences and Humanities headed first by Karl Fuchs and then by Friedemann Wenzel. Since 2009 the WSM is maintained at the GFZ Helmholtz Centre for Geosciences. The WSM database release 2025 contains 100,842 data records within the Earth’s crust. The data are provided in two formats: Excel-file (wsm2025.xlsx) and comma separated fields (wsm2025.csv). Data records with reliable A-C quality are displayed in the World Stress Map (doi:10.5880/WSM.2025.002). Further detailed information on the WSM quality ranking scheme 2025, guidelines for the analysis of borehole logging data, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org. The database structure and content is explained in the WSM Technical Report TR 25-01 (https://doi.org/10.48440/wsm.2025.001).
Understanding the contemporary stress state in rock volumes is crucial for applications such as reservoir management, geothermal energy, and underground storage. Geomechanical-numerical modelling, which predicts the 3D stress state based on geological structures, density distributions, and elastic properties, requires calibration using stress magnitude data records acquired in-situ. However, these data records can include outliers—stress measurements significantly deviating from expected values due to errors or localized geological anomalies. These outliers can skew model calibrations, leading to inaccurate predictions of boundary conditions and stress magnitudes, particularly in sets with limited numbers of data records. A systematic approach to identifying and handling outliers is essential to mitigate inaccuracies. The Python-based script DOuGLAS (Detection of Outliers in Geomechanics using Linear-elastic Assumption and Statistics) was developed to address this challenge. The software is part of the FAST (Fast Automatic Stress Tensor) suite of programs. Its function is to identify outliers in sets of stress magnitude data records by assessing the respective impact of individual data records on boundary condition predictions, using iterative combinations of data records. Results are analysed through dimensionality reduction and statistical scoring, providing visual and quantitative tools for outlier detection. The script aids users in improving model reliability by identifying and addressing anomalous data. It supports sets of different numbers of stress magnitude data records and integrates seamlessly with tools such as Tecplot 360 EX and GeoStress. This manual provides a comprehensive guide for using DOuGLAS, interpreting its outputs, and understanding its application in geomechanical modeling.
Understanding the contemporary stress state in rock volumes is crucial for applications such as reservoir management, geothermal energy, and underground storage. Geomechanical-numerical modelling, which predicts the 3D stress state based on geological structures, density distributions, and elastic properties, requires calibration using stress magnitude data records acquired in-situ. However, these data records can include outliers—stress measurements significantly deviating from expected values due to errors or localized geological anomalies. These outliers can skew model calibrations, leading to inaccurate predictions of boundary conditions and stress magnitudes, particularly in sets with limited numbers of data records. A systematic approach to identifying and handling outliers is essential to mitigate inaccuracies. The Python-based script DOuGLAS (Detection of Outliers in Geomechanics using Linear-elastic Assumption and Statistics) was developed to address this challenge. The software is part of the FAST (Fast Automatic Stress Tensor) suite of programs. Its function is to identify outliers in sets of stress magnitude data records by assessing the respective impact of individual data records on boundary condition predictions, using iterative combinations of data records. Results are analysed through dimensionality reduction and statistical scoring, providing visual and quantitative tools for outlier detection. The script aids users in improving model reliability by identifying and addressing anomalous data. It supports sets of different numbers of stress magnitude data records and integrates seamlessly with tools such as Tecplot 360 EX and GeoStress. This manual provides a comprehensive guide for using DOuGLAS, interpreting its outputs, and understanding its application in geomechanical modeling.
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 classical way to model the stress state in a rock volume is to estimate displacement boundary conditions that minimize the deviation of the modelled stress state with respect to model-independent stress information such as stress magnitude data. However, these data records are usually subject to significant uncertainties and measurement errors. Hence, it has to be expected that not all stress magnitude data records are representative and can be used in a model. In order to identify unreliable stress data records, the stress state that is based on individual data records is solved and compared with observations at a few discrete locations. While this method works, it is not efficient in that most of the solved model scenarios will be discarded. The solving of the entire model consumes immense amount of computation time for a high-resolution model. Yet, the stress state is required at only a very limited number of locations. For linear geomechanical models it is sufficient to estimate the stress state from three model scenarios with arbitrary, but different displacement boundary conditions. These three results can be used to estimate analytically using a linear regression at discrete points stress states based on user-defined boundary conditions. The tool Fast Automatic Stress Tensor Estimation (FAST Estimation) is a Python function that automatizes this approach. FAST Estimation provides very efficiently the stress states at pre-defined locations for all possible boundary conditions. It does not provide the continuous stress field as provided by a solved geomechanical model. Instead, it is a cost-efficient solution for the rapid assessment of stress states at a limited number of discrete locations based on pre-defined boundary conditions.
Stress maps show the orientation of the current maximum horizontal stress (SHmax) in the earth's crust. Assuming that the vertical stress (SV) is a principal stress, SHmax defines the orientation of the 3D stress tensor; the minimum horizontal stress Shmin is than perpendicular to SHmax. In stress maps SHmax orientations are represented as lines of different lengths. The length of the line is a measure of the quality of data and the symbol shows the stress indicator and the color the stress regime. The stress data are freely available and part of the World Stress Map (WSM) project. For more information about the data and criteria of data analysis and quality mapping are plotted along the WSM website at http://www.world-stress-map.org. The stress map of Great Britain and Ireland 2022 is based on the WSM database release 2016. All data records have been checked and we added a number of new data from earthquake focal mechanisms from the national earthquake catalog and borehole data. The number of data records has increased from n=377 in the WSM 2016 to n=474 in this map. Some locations and assigned quality of WSM 2016 data were corrected due to new information. The digital version of the map is a layered pdf generated with GMT (Wessel et al., 2019) using the topography of Tozer et al. (2019). We also provide on a regular 0.1° grid values of the mean SHmax orientation which have a standard deviation < 25°. The mean SHmax orientation is estimated using the tool stress2grid of Ziegler and Heidbach (2019). For this estimation we used only data records with A-C quality and applied weights according to data quality and distance to the grid points. The stress map is available at the landing page of the GFZ Data Services at http://doi.org/10.5880/WSM.GreatBritainIreland2022 where further information is provided.
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 homogene-ous properties. However, variable rock properties are observed and expected within the same geological unit. Even within small volumes large variabilities may occur. 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® or the MOOSE Framework. 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 indi-vidually defined for each of the three rock properties in each geological layer. For each unit or unit subset HIPSTER generates a normal or uniform distribution for each rock property. From these distributions for each single element or subset of elements HIPSTER draws indi-vidual rock properties and writes them to a separate material file. This file defines differ-ent material properties for each element. The file is included in the geomechanical-numerical analysis solver deck and the numerical model is solved as usual. HIPSTER is fully documented in the associated data report (Ziegler, 2021, https://doi.org/10.48440/wsm.2021.001) and can also be accessed at Github (http://github.com/MorZieg/hipster).
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 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.
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