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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 dataset is an extended and updated version of the homogenized regional earthquake catalogue of the Marmara region, north-western Turkey, presented in Wollin et al. (2018) and Becker et al. (2023). It is built on the regional Turkish seismicity catalogues provided by AFAD (Disaster and Emergency Management Presidency of Turkey) and KOERI (Kandilli Observatory and Earthquake Research Institute) and spans the time interval 2021-2023. All events available in these two catalogues in the wider Marmara region were combined and duplicate events removed. A total of 2242 events having at least 6 P- and/or S-picks were located using the NLLoc software (Lomax et al., 2000, 2009) in Octtree mode utilizing automatic picks obtained with the PhaseNet algorithm (Zhu & Beroza, 2019) for all available waveforms. The magnitude range is between M0.5 and M5.1 and covers mainly the area 40.00S-41.25S and 27.00E-30.00E which was used as search region for the regional catalogs. The full description of the data and methods is provided in the data description file.
Greece is Europe’s most seismically active nation, as it is being deformed by an active subduction system and one of the world’s fastest-spreading rifts. Onshore active faults pose seismic hazard that cannot be reliably assessed in the absence of a comprehensive map of potential earthquake sources. Here, we use high-resolution Digital Elevation Models (DEMs), in conjunction with hillshades and slope models, to map and characterise faults in Greece at a scale of 1:25000. The Active Faults Greece (AFG) database records a total of 3815 fault-traces assigned to 892 interpreted faults. Of the AFG traces, 53% were mapped here for the first time, with their geometries and slip-sense constrained by displacement of landscape features. AFG includes >2000 active and 1632 probably active fault-traces, while 30 traces result from historic surface-rupturing earthquakes since 464 BC. About 57% of faults exhibit strong depositional control (DC) on sedimentation patterns, with active faults being characterised by approximately equal numbers of sharp (32%), moderate (29%) and rounded (29%) scarps. AFG is the first fault database in Greece generated using nationwide interpretation of geomorphology and has applications in paleoseismology, seismic-hazard assessment, mineral-resources exploration, and resilience planning. Data Access: - Download archive version via GFZ Data Services (upper left) - Web-Map Server: https://experience.arcgis.com/experience/a6c85b1edf9d4d17a3f01a70cef6d2b2 - GIS Users: https://services2.arcgis.com/T7iULq65Kp9Elquk/arcgis/rest/services/Active_Faults_Greece/FeatureServer - Layerfiles for use in ArcGIS Pro and QGIS: https://noaig.maps.arcgis.com/sharing/rest/content/items/4b93c25b931744dabc4851abf9c8ae38/data
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.
Numerical model supporting the article: "Uplifted marine terraces at active margins: understanding the effects of sea reoccupation and coseismic uplift on uplift rate calculation. The forward numerical model reproduces the evolution of an uplifting margin subject to sea erosion. The age-mixing resulting from reoccupation and the likelihood of missing terraces along a staircase sequence increase the inaccuracy of terrace ages assigned through geometrical cross correlation; this may result in erroneous uplift rates and consequent misinterpretation of the uplift evolution. Further research is needed to explore whether vertical displacement reproducing the full seismic cycle, inclusive of both permanent and elastic deformation, and variable uplift rates, have a similar relevance in shaping the geometry of terrace sequences. The code provides the possibility to have steady uplift, i.e. aseismic and constant over time, or coseismic uplift, i.e. given by instantaneous vertical displacement, reproducing earthquakes. It is possible to define time intervals having different uplift rate values, or different uplift modes (aseismic and seismic periods), or vary the characteristic of the coseismic uplift, such as recurrence intervals and coseismic uplift displacement. The coseismic uplift can also be superimposed to a background uplift rate. All values can be of positive or negative sign. The user can define which variable values are saved in the model output, and these include parameters such as the terrace age and the reoccupation tracker. In the repository we include three sea level curves, but any other sea level curve provided by the user can be used to run the model. The parameter values used in the manuscript models are described in the Supplementary Information file of the manuscript. The data provided in txt format report data published by Saillard et al. (2011) and additional calculations, which have been used for the case study of the manuscript. The model scripts are written in Julia language and can be used to reproduce marine terraces formation at coastal margins subject to uplift. The scripts are organized as Github repository (https://github.com/albert-de-montserrat/LEM1D). Movies S1 to S8 provide a qualitative illustration of the terrace evolution under different uplift conditions.
The knowledge about the distribution of active faults is crucial for hazard assessment (Costa et al., 2020; Santibáñez et al., 2019; Wesnousky, 1986) but also provides insights into tectonic control on hydrological processes (Binnie et al., 2020; Jeffery et al., 2013; Pan et al., 2013) or georesource distribution (Goldsworthy & Jackson, 2000; Viguier et al., 2018). Furthermore, tectonically driven topographic uplift and its impact on climate (Armijo et al., 2015; Houston & Hartley, 2003; Rech et al., 2019; Zhisheng et al., 2001) can be better understood if a systematically mapped fault database exists. Here we present an active fault database, as well as the distribution of drainages, for an area between 18.50°S and 19.45°S in Northern Chile forearc, which were systematically mapped in the framework of the project “Cluster C05-Tectonic Geomorphology: Adaptation of drainage to tectonic forcing” of the CRC1211- Earth Evolution at the Dry Limit. The Central Andes forearc at this latitude is located at a highly tectonically active convergent margin and hosts major earthquakes not only on the plate boundary itself (e.g., Métois et al., 2016), but also in the overriding crust (e.g., Comte et al., 1999). It comprises, from west to east, the Coastal Cordillera, Longitudinal Valley and the Western Flank of the Altiplano, showing an impressive amount of topographic variability of ca. 4000 m. Nevertheless, Neogene crustal tectonic structures and surface deformation are poorly documented. The overall landscape appears as a gentle west-sloping pediplain dissected by deep transversal canyons (quebradas), which reach the current Pacific Ocean (Mortimer, 1980). The Longitudinal Valley is a sedimentary basin filled with 432 to 2000 m of Tertiary to Quaternary deposits derived from the Altiplano in the east as well as the Coastal Cordillera in the west (García et al., 2017). Its surface is composed by a multiphase planation surface called the Pacific Paleosurface (PPS), which distribution is suggested to be controlled by crustal tectonics (Evenstar et al., 2017). Depending on the low ratio of tectonic displacement rate to sedimentation rate, many active faults are hidden and only a specialized approach of high-resolution fault mapping, together with a morphometric analysis of the drainage pattern provides systematic information about the distribution of active faults, folds and related structures. The present fault database is the result of creating a comprehensive catalogue of faults classified by the age of last proven/probable tectonic activity. This is accompanied by a compilation of existing age data and a map of drainage pattern. These datasets were compiled in QGIS 3.16.5 (https://www.qgis.org) and are available as. gpkg for GIS applications and as .kml formats to be visualized in Google Earth.
The dataset is an extended and updated version of the homogenized regional earthquake catalogue of the Marmara region, north-western Turkey, presented in Bohnhof et al. (2017) and Wollin et al. (2018). It is built on the regional Turkish seismicity catalogues provided by AFAD (Disaster and Emergency Management Presidency of Turkey) and KOERI (Kandilli Observatory and Earthquake Research Institute) and spans the time interval 2006-2020. All events available in these two catalogues in the wider Marmara region were combined and dublicate events removed. A total of 13812 events having at least 6 P- and/or S-picks were located using the NLLoc software (Lomax et al., 2000, 2009) in Octtree mode utilizing automatic picks (see Wollin et al., 2018 for details) for all available waveforms. The magnitude range is between M0.3 and M5.7 with time-variable magnitude of completeness and covers the area 39.70S-41.50S and 26.0E-30.65E. The full description of the data and methods is provided in the data description file.
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).
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