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A sequence of three strong (MW 7.2–6.4) and several moderate (MW 4.4–5.7) earthquakes struck the Pamir Plateau and surrounding mountain ranges of Tajikistan, China, and Kyrgyzstan in 2015–2017. With a local seismic network in operation in the Xinjiang province of China since August 2015 (FDSN code 8H; Yuan et al., 2018a), an aftershock network on the Pamir Plateau of Tajikistan since February 2016 (FDSN code 9H; Yuan et al., 2018b), and additional permanent regional seismic stations (FDSN code TJ; PMP International, 2005; XJ network; SEISDMC, 2021), we were able to record the succession of the fore-, main-, and aftershock sequences at local distances with good azimuthal coverage. We here provide P and S body wave arrival times of the 11,784 relocated seismic events and additional arrival times of 18,011 seismic events that could not be located with precision. The ASCII QuakeML files (.xml; https://quake.ethz.ch/quakeml/QuakeML) consist of seismic arrival times, station and network codes, nominal arrival time uncertainties, localization residuals, and corresponding preliminary event locations. The ASCII NonLinLoc Hypocenter-Phase files (.hyp; http://alomax.free.fr/nlloc/ -> Formats -> NLLoc Hypocenter-Phase file) consist of seismic arrival times, station codes, nominal arrival time uncertainties, localization residuals, ray take-off angles and corresponding preliminary event locations.
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).
This dataset is supplemental to the paper Wiesman et al. (submitted) and contains data on the density of dislocations and their stress fields in olivine rocks deformed via laboratory experiments. The data were used to investigate how the quality of diffraction patterns obtained via electron backscatter diffraction (EBSD) affect the stress maps and geometrically necessary dislocation (GND) maps obtained via analysis with high-angular resolution electron backscatter diffraction (HR-EBSD). These results can be used to optimize the patterns collected during EBSD to reduce noise in the HR-EBSD analysis. Data are provided in a zip folder and include: • Measurements of lattice orientation via EBSD: six raw .ctf files and six processed .ctf files of regions mapped with HR-EBSD • Examples of electron backscatter diffraction patterns used to calculate radial power spectra: 12 .tiff files of diffraction patterns • Densities of geometrically necessary dislocations from the HR-EBSD analysis: six .txt files of processed data • Residual stress heterogeneity also determined from HR-EBSD analysis: six .txt files of processed data Data types and the number of frames averaged are also indicated in the file names. Files are organized into folders by the number of frames averaged. A full description is available in the data description file.
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.
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.
This dataset is supplemental to the paper Wiesman et al. (In prep) and contains data on the density of dislocations and their stress fields in olivine from laboratory experiments to examine transient creep in olivine. The data were used to characterize the microstructural evolution that occurs during transient creep in olivine. These results can be used to test and calibrate microphysical models for transient creep that will be used to describe how Earth’s mantle responds to changes in stress caused by earthquakes and as melting glaciers. Data are provided in a zip folder and include: • Mechanical data from each experiment: ten .txt files of raw data, ten .txt files of processed data • Measurements of lattice orientation via EBSD: ten .ctf files of large area EBSD maps and ten .ctf files of regions mapped with HR-EBSD • Densities of geometrically necessary dislocations from the HR-EBSD analysis – ten .txt files of processed data • Residual stress heterogeneity also determined from HR-EBSD analysis – 20 .txt files of processes data • Forescatter electron images of decorated dislocations – 49 .tiff files and 49 .png files of decorated dislocations, 44 .pngs of counted dislocations, and one .txt file documenting the counted dislocations Data types and sample numbers are also indicated in the file names. Files are organized into folders by sample. Data types and sample numbers are also indicated in the file names. A full description is available in the data description file.
BayStress4 is a package of MatLab routine, designed to constrain the state of stress of a volcanic system by means of posterior Probability Density Functions (PDFs) of the stress tensor components. To do so, it employs the model of three-dimensional (3D) dyke pathways developed by Mantiloni et al., 2023 (SAM: Simplified Analytical Model of dyke Pathways in Three Dimensions) to match the known locations of past eruptive vents to the known or assumed volume in the subsurface ("Dyke nucleation zone" or "D") where their parent dykes nucleated from. This is achieved by a) using SAM to backtrack dyke pathways from the vents down through the crust for a given stress model; b) quantifying the intersection between such pathways and D through a misfit function; c) using this procedure to run a Markov Chain Monte Carlo (MCMC) algorithm to sample the stress parameters' space. The posterior information provided by the stress inversions can then be used to produce forward simulations of dyke pathways with SAM and forecast the surface distribution of future eruptive vents across the volcanic system. This repository contains InVent4Cast, a package of MatLab routines designed to constrain the state of stress of a volcanic system by means of posterior Probability Density Functions (PDFs) of the stress tensor components. To do so, it employs the model of three-dimensional (3D) dyke pathways developed by Mantiloni et al., 2023a (SAM: Simplified Analytical Model of dyke Pathways in Three Dimensions) to match the known locations of past eruptive vents to the known or assumed volume in the subsurface ("Dyke nucleation zone" or "D") where their parent dykes nucleated from. This is achieved by a) using SAM to backtrack dyke pathways from the vents down through the crust for a given stress model; b) quantifying the intersection between such pathways and D through a misfit function; c) using this procedure to run a Markov Chain Monte Carlo (MCMC) algorithm to sample the stress parameters' space. The posterior information provided by the stress inversions can then be used to produce forward simulations of dyke pathways with SAM and forecast the surface distribution of future eruptive vents across the volcanic system. The repository also collects data, figures and results of the application of InVent4Cast to some of the synthetic scenarios of dyke pathways in calderas presented by Mantiloni et al., 2023a. These results were detailed and discussed by Mantiloni et al., 2024a, to which the reader is referred for further information. The synthetic scenarios include numerical models of crustal stress state, focusing on gravitational loading/unloading due to topography and tectonic processes as the dominant stress sources. These stress sources are accounted for by a set of stress parameters. Results include posterior probability density functions (PDFs) of such stress parameters after applying the stress inversion to the scenarios, as well as probability maps of eruptive vent opening across the synthetic volcanic areas. Synthetic scenarios, stress inversions and vent forecasts were produced between May 2022 and November 2023.
The World Stress Map (WSM) database 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 German Research Centre for Geosciences and since 2012 the WSM is a member of the ICSU World Data System. All stress information is analysed and compiled in a standardized format and quality-ranked for reliability and comparability on a global scale.The WSM database release 2016 contains 42,870 data records within the upper 40 km of the Earth’s crust. The data are provided in three formats: Excel-file (wsm2016.xlsx), comma separated fields (wsm2016.csv) and with a zipped google Earth input file (wsm2016_google.zip). Data records with reliable A-C quality are displayed in the World Stress Map (doi:10.5880/WSM.2016.002). Further detailed information on the WSM quality ranking scheme, guidelines for the various stress indicators, and software for stress map generation and the stress pattern analysis is available at www.world-stress-map.org.VERSION HISTORY:Version 1.1. (15 June 2019): updated version of the zip-compressed Google Earth .kml (wsm2016_google.zip) with a new URL of the server.
This dataset presents the raw data of an experimental series of analogue models performed to investigate the influence of inherited brittle fabrics on narrow continental rifting. This model series was performed to test the influence of brittle pre-existing fabrics on the rifting deformation by cutting the brittle layer at different orientations with respect to the extension direction. An overview of the experimental series is shown in Table 1. In this dataset we provide four different types of data, that can serve as supporting material and for further analysis: 1) The top-view photos, taken at different steps and showing the deformation process of each model; they can be used to interpret the geometrical characteristics of rift-related faults; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models, allowing for quantitative analysis of the fault pattern. 3) Short movies built from top-view photos which help to visualize the evolution of model deformation; 4) line-drawing of fault and fracture patters to be used for fault statistical quantification. Further details on the modelling strategy and setup can be found in Corti (2012), Maestrelli et al. (2020), Molnar et al. (2020), Philippon et al. (2015), Zwaan et al. (2021) and in the publication associated with this dataset. Materials used for these analogue models were described in Montanari et al. (2017) Del Ventisette et al. (2019) and Zwaan et al. (2020).
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