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The data set includes the Digital Image Correlation (DIC) results for four experiments of releasing bends along dextral strike-slip faults that were performed at the University of Massachusetts at Amherst (USA). Gabriel et al. (in prep.) used the DIC data sets to investigate how releasing bend fault systems evolve within different strength wet kaolin. Information on the experimental set up and methods can be found in the main text and supplement to Gabriel et al. (in prep.). The data here include the incremental displacement time series, strain animation and surface elevation data at the end of the two experiments with different clay strength, which are presented within Gabriel et al. (in prep). We also include in this data repository incremental displacement time series and strain animations from two experiments that repeat the conditions of the experiments featured in Gabriel et al. (2025).
This data publication includes geologic-map and structural-field data that complement a structural analysis of intra-montane basins of the southwestern Tian Shan of Central Asia. The southwestern Tian Shan is defined as that part of the Tian Shan west of the Talas-Fergana Fault Zone that is located at the junction with the Pamir and the Afghan-Tajik Basin and stretches to the Fergana Basin in the north. It also includes an ArcGIS-Geodatabase with the shapefiles of the digitized stratigraphy and faults for the regional geological maps. The data are supplementary material to Trilsch et al. (2025): “Southwestern Tian Shan: 1. Deformation of Cenozoic Intra-montane Basins and Intervening Basement Ranges in Front of the Indian Mantle Indenter” (Tectonics; doi: added when published). One Figure, provided as pdf file and ArcGIS shapefiles, provides a map of the southwestern Tian Shan that compiles structures that were active or potentially-active during the Cenozoic. Reliable Cenozoic structures affect Mesozoic-Cenozoic strata; potentially-Cenozoic ones displace younger over older Paleozoic strata, or show opposite vergence within a sequence of consistently-verging Paleozoic nappes. The other figures provide maps, structural-data stereoplots, field pictures, and outcrop sketches of several intra-montane basins of the southwestern Tian Shan. The supplementary material is useful for researchers aiming to study the geoscience of the western (Tajik, Uzbek, Kyrgyz) Tian Shan.
This data set includes videos depicting the evolution of nine numerical tectonic models simulating rift-inversion orogens. For these models we apply the 2D thermo-mechanical geodynamic code ASPECT, coupled with FastScape for the inclusion of surface processes. Using the results from these models, we examine mantle serpentinization in rift-inversion orogens, and their associated natural hydrogen gas (H2) potential. Detailed descriptions of the model set-up and results can be found in Zwaan et al. (2025) in Science Advances.
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.
In this dataset we provide top-view photos and perspective photos (to create topographic data, i.e. Digital Elevation Models, DEMs) documenting analogue model deformation. For more details on modelling setup, experimental series Wang et al. (2021), to which this dataset is supplementary material. For details on analogue materials refer to Del Ventisette et al., 2019, Maestrelli et al. (2020). The analogue modelling experiments were carried out at the TOOLab (Tectonic Modelling Laboratory) of the Institute of Geosciences and Earth Resources of the National Research Council of Italy, Italy, and the Department of Earth Sciences of the University of Florence. The laboratory work that produced these data was supported by the European Plate Observing System (EPOS) and by the Joint Research Unit (JRU) EPOS Italia. Additional analysis, following the original work, was supported by the “Monitoring Earth’s Evolution and Tectonics” (MEET) project
This dataset contains a series of analog models for comparing and testing positive tectonic inversion mechanisms and wedge structure formation. Furthermore, it includes a 2-D seismic reflection profile that can be compared with the models presented here. Finally, several photos of some structural features that cab be associated with wedge structure are shown. Both seismic lines and photos are located on a segment of Andean forearc, specifically, in the eastern Domeyko Cordillera and the Salar de Atacama Basin in northern Chile. Specifically, the models were deformed under extensional and compressional conditions, inducing a positive tectonic inversion, using a pure/simple-shear deformational apparatus. Our models intended to simulate the tectonic conditions presented in López et al. (2022), which illustrated the structural setting of the Domeyko Cordillera as resulting from the interplay between positive inversion tectonics and pure shortening faulting. Moreover, our models simulated three geological environments that developed sequentially through time: (a) syn-rift sedimentation, (b) post-rift and pre-shortening sedimentation, and (c) syn-shortening sedimentation. Post-rift and syn-shortening sedimentation incorporated a ductile layer (PDMS) during the shortening phase, simulating the presence of evaporitic deposits (i.e., gypsum) to test the conditions that could have controlled the formation of pure-shortening-related structures in the case study under consideration.
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).
This dataset shows the original data of a series of enhanced-gravity (centrifuge) analogue models, which were performed to test the influence of the pre-existing fabrics in the brittle upper crust on the evolution of structures resulting from oblique rifting. The obliquity of the rift (i.e., the angle between the rift axis and the direction of extension) was kept constant at 30° in all the models. The main variable of this experimental series was the orientation of the pre-existing fabrics (indicated as the angle between the trend of the fabric and the orthogonal to extension), which varied from 0° to 90° (i.e., from orthogonal to parallel to the extension direction). The inherited discontinuities were reproduced by cutting with a knife through the top brittle layer of models. An overview of the experimental series is shown in Table 1. In this dataset, four different data types are provided for further analysis: 1) Top-view photos of model deformation, taken at different time intervals 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 analogue models, allowing for quantitative analysis of the fault pattern. 3) Movies of model deformation, built from top-view photos, which help to visualize the evolution of model deformation; 4) Faults line-drawings to be used for statistical quantification of rift-related structures. Further information on the modelling strategy and setup can be found in the publication associated to this dataset and in Corti (2012), Philippon et al. (2015), Maestrelli et al. (2020), Molnar et al. (2020), Zwaan et al. (2021), Zou et al. (2023). Materials used to perform these enhanced-gravity analogue models were described in Montanari et al. (2017), Del Ventisette et al. (2019) and Zwaan et al. (2020).
This data set provides a series of experiments from ring-shear tests (RST) on various materials that are used at several laboratories worldwide. The data contains the results of slide-hold-slide tests and the processed outputs of standardized ring shear tester data from related publications. Additionally, microscopy images of the materials under plain and polarized light are provided. The time dependent restrengthening of the materials is quantified using slide-hold-slide tests. This restrengthening has implications on the reactivation potential of granular shear zones in analogue models. With the provided software we first analyze the experimental data and then compare the angles and stresses needed to reactivate normal faults in the materials. We find that while healing rates are low, the majority of samples can not reactivate normal faults that are generated through extension of an analogue model.
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