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This dataset compiles quantitative outputs from eight sandbox experiments conducted under different boundary conditions (differential extension, strong blocks, and a weak zone). It contains 3-D scanning–derived digital elevation models (DEMs) from the final stage of experiments simulating the V-shaped opening of the South China Sea. In addition, it includes particle image velocimetry (PIV) products at four extension states (25 mm, 50 mm, 75 mm, and 100 mm), together with the plotting codes used to generate the figures.
This data set includes results from a total of 13 analogue tectonic models aimed at simulating the activation of tectonic lineaments associated with the Main Ethiopian Rift in eastern Africa. We use a model set-up based on previous work by Zwaan et al. (2021, 2022). This set-up involves a velocity discontinuity (VD, i.e., the edge of a mobile base plate) to induce extension in the overlying brittle- and viscous model materials representing the upper and lower crust, respectively. Additional structural weaknesses (seeds) at the base of the brittle layer serve to represent activated tectonic weaknesses in nature. Model parameters (different VD and seed orientation, and different seed diameters) are summarized in Table 1. The model results presented in this data publication are obtained through Digital Image Correlation (DIC) and Structure-from-Motion (SfM) analyses. A more detailed description of model set-up, model results, and their interpretation can be found in Zwaan et al. (2025)
This earthquake catalog was constructed using a combination of artificial intelligence and traditional methods for phase picking, phase association, and earthquake relocation. It covers the period from January 1, 2017, to February 5, 2023—one day prior to the Mw 7.8 earthquake that struck Türkiye. The dataset includes three subsets: 1) Raw Catalog: Comprises 14,128 events obtained from the full association and relocation process, without filtering based on event type or location quality. 2) Earthquake Catalog: Comprises 5,721 tectonic events with well-constrained hypocenters (68% confidence ellipsoid semi-major axis < 8 km and depth < 15 km). 3) Anthropogenic Catalog: Comprises 1,695 human-induced events, primarily quarry blasts, also with well-constrained hypocenters (68% confidence ellipsoid semi-major axis < 8 km and depth < 15 km).
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 dataset contains three seismicity catalogs covering the first 5 days of the aftershock sequence of the Mw 7.8 Karamanmaraş and Mw 7.6 Elbistan earthquakes that occurred in Türkiye on February 6th, 2023. The catalogs are derived from machine learning (ML) approaches operating on continuous data from 38 permanent seismological stations covering the area of the aftershock sequence and span the time interval 06.02.2023-10.02.2023. The seismological stations are operated by AFAD (Disaster and Emergency Management Presidency of Turkey) and KOERI (Kandilli Observatory and Earthquake Research Institute). Automatic P- and S-phase picks were obtained using the deep learning PhaseNet software (Zhu & Beroza, 2019), and either GaMMA (Zhu et al., 2022) or GENIE (McBrearty & Beroza, 2023) routines were used to associate these phases into seismic events. The probabilitic NLLoc earthquake location software (Lomax et al., 2009) was used to produce single event locations and final relative relocations were obtained after applying the hypoDD software (Waldhauser & Ellsworth, 2000). This resulted in two single event location NLLoc aftershock catalogs based on GaMMA and GENIE event association and containing 17,550 and 14,805 event detections in the time interval 06.02.2023 01:18 UTC - 11.02.2023 00:00 UTC, respectively. The hypoDD based catalog of better constrained relative relocations contains 5,215 events. The magnitude range is between M-0.1 and M6.9 with time-variable magnitude of completeness. The catalog covers the area 36.00S-39.00S and 35.40E-40.00E. The full description of the data and methods is provided in the data description file.
For the determination of the exact location and drilling geometry of the ICDP expedition 5071 DIVE (Drilling the Ivrea-Verbano zonE) phase 1 boreholes, we have carried out a series of active seismic experiments to image the subsurface at high resolution (Greenwood et al., 2024). The two drilling sites of DIVE phase 1 are located in the Ossola valley (Figure 1) in the Central part of the Ivrea-Verbano zone, one in Megolo di Mezzo (5071_1_A: IGSN ICDP5071EH10001, Münthener, 2024a) and the other near Ornavasso (5071_1_B: IGSN ICDP5071EH30001, Münthener, 2024a). A total of 4 seismic reflection surveys, one in Megolo, two in Ornavasso (Ornavasso primary and Or-navasso secondary), and a transect crossing from Premosello-Chiovenda south towards Megolo, make up the MicrO-SEIZE (MOS) data base. The MOS surveys were conducted over a period of 12 days mid to late June 2019, utilizing a 26,000 lb (12,247 kg) EnviroVibe2™ vibrator source. Survey planning utilised existing roads, pathways, and open grass fields to cover as much area as possible around the borehole sites, to reduce the envi-ronmental impact on cultivation, and to ease operation logistics. A full description of the data can be found in Greenwood et al. (2024) and the data description file accessible via the data download link.
This dataset includes volumetric data sets from a Digital Volume Correlation (DVC) analysis for recreating images of a re-analyzed analogue models previously presented in (Zwaan et al., 2018). Using a brittle-viscous two-layer setup, this experiment focused on the evolution of a rift-pass structure. On top of the viscous layer, two viscous seeds are placed with a right-stepping stair-case offset to simulate two propagating rift segments, confining a central rift-pass block (Fig. 1). The selected model was analyzed by means of Digital Volume Correlation (DVC) applied on X-Ray computed tomography (XRCT) volumes. The data set includes DVC data in the form of .mat files for incremental (i.e., 20 min intervals) and cumulative displacement components. In addition, this dataset provides a MATLAB script for 1) recreating volumetric displacement sets of subsequent time steps 2) calculating finite stretches and 3) rigid-body rotations. The used experiment was performed at the Tectonic Modelling Laboratory of the University of Bern (UB). DVC analysis was performed at the Royal Holloway University London (RHUL). The model consists of a two-layer brittle-viscous set up with a total thickness of 8 cm and the set up lies on top of a 5 cm thick foam-plexiglass base with a length and width of 800 mm by 305 mm, respectively. Before model construction, the foam-plexiglass assemblage is placed between longitudinal side walls and expands during the course of the experiment as the mobile sidewalls move apart. The applied divergence velocity is 7.5 mm/h and with has an orthogonal direction with respect to the viscous seeds. This results in a maximum displacement of 30 mm after a total run time of 4h. Detailed descriptions of the experiment, mechanical properties as well as monitoring techniques can be found in Schmid et al. (2024).
This dataset provides results from rheological tests of glucose syrup from two suppliers tested within the EPOS Multi-scale Laboratories (MSL) trans-national access (TNA) program 2019 at the Laboratory of Experimental Tectonics (LET), Univ. Roma TRE, Italy. Syrups Glucowheat 45/81 (GW45) and Glucowheat 60/79 (GW60) are produced by Blattmann Schweiz AG, Switzerland (2019 batch). Syrups GlucoSweet 44 (GS44) and GlucoSweet 62 (GS62) are produced by ADEA (Amidi Destrini ed Affini), Italy (2019 batch) . The four tested glucose syrups are labeled according to their DE value (dextrose equivalent value). For tested products from Blattmann Schweiz AG, the second number refers to the weight percentage of dry substance. Glucose syrup GS44 is used in full lithospheric scale analogue experiments at the Tectonic Modelling Lab (TecLab) at the University of Bern, Switzerland as a low-viscosity material simulating the asthenospheric mantle lithosphere to provide isostatic equilibration. The materials have been analyzed using a MCR301 Rheometer (Anton Paar) equipped with parallel plates geometry and rotational regime . To prevent the evaporation of the samples during the measurements, an external water-lock device has been used.
This data publication contains (i) a slab model of the Cascadia subduction zone, derived from receiver functions, parameterized as depth to the three interfaces: t (top), c (central) and m (Moho), in NetCDF format; (ii) the station measurements of all parameters in the model in tabular and Raysum model file format; (iii) the raw receiver functions in SAC format; and (iv) auxiliary scripts for loading and plotting the data. A total of 45,601 individual receiver functions recorded at 298 seismic stations distributed across the Cascadia forearc contributed to the slab model. For each station, 100 s recordings symmetric about the P -wave arrival (i.e. 50 s noise and 50 s signal) of earthquakes with magnitudes between 5.5 and 8, in the distance range between 30 and 100 degree, were downloaded from the Incorporated Research Institutions for Seismology (IRIS) data center, the Northern California Earthquake Data Center (NCEDC), and the Natural Resources Canada Data Center (NRCAN). After quality control, radial and transverse receiver functions were computed through frequency-domain simultaneous deconvolution, with an optimal damping factor found through generalized cross validation. The continental forearc and subducting slab were parameterized as three layers over a mantle half-space, with the subduction stratigraphy bounding interfaces labeled as t (top), c (central) and m (Moho). Synthetic receiver functions were calculated through ray-theoretical modeling of plane-wave scattering at the model interfaces. The thickness, S -wave velocity (VS) and P - to S -wave velocity ratio (VP/VS) of each layer, as well as the common strike and dip of the bottom two layers and the top of the half space (in total 11 parameters) were optimized simultaneously through a simulated annealing global parameter search scheme. The misfit was defined as the anti-correlation (1 minus the cross-correlation coefficient) between the observed and predicted receiver functions, bandpass filtered between 2 and 20 s period duration. In total, 171, 143 and 137 quality A nodes were determined to constrain the t, c and m interfaces, respectively. At the trench, 105 nodes at 3 km below the local bathymetry were inserted to constrain the t and c interfaces, and at 6.5 km deeper to constrain the m interface, representing typical sediment and igneous crustal thicknesses. A spline surface was fitted to these nodes to yield margin-wide depth models. The spline coefficients were found using singular value decomposition, with the nominal depth uncertainties supplied as weights. The solution was damped by retaining the 116, 117, and 116 largest singular values for the t, c and m interfaces, respectively, based on analysis of L-curves and the Akaike information criterion. The data set is the supplemental material to Bloch, W., Bostock, M. G., Audet, P. (2023) A Cascadia Slab Model from Receiver Functions. Geochemistry, Geophysics, Geosystems.
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