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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.
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.
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 data publication includes a grid composed by contiguous 25 x 25 km square elements covering the Italian area and each parametrized by 1) the maximum length of faults included within the cell, 2) the maximum magnitude from instrumental seismic data, 3) the maximum magnitude from historical seismic data, 4) the maximum magnitude calculated from fault length using empirical scaling laws.This collection represents the basis to a work (Trippetta et al., 2019) aiming to test a fast method comparing the geologic (faults) and the seismologic (historical-instrumental seismicity) information available for a specific region. To do so, (1) a comprehensive catalogue of all known faults and (2) a comprehensive catalogue of earthquakes were compiled by merging the most complete available databases; (3) the related possible maximum magnitudes were derived from fault dimensions, upon the assumption of seismic reactivability of any fault; (4) the calculated magnitudes were compared with earthquake magnitudes recorded in historical and instrumental time series.Faults: to build the dataset of faults for Italy, the following databases were merged: (1) the entire faults collection after the Italian geological maps at the 1:100,000 scale (available online at www.isprambiente.it); (2) the faults compilation from the structural model of Italy at the 1:500,000 scale (Bigi et al., 1989); (3) faults provided in the ITHACA-Italian catalogue of capable faults (Michetti et al., 2000); and (4) the inventory of active faults of the GNDT (Gruppo Nazionale per la Difesa dai Terremoti, Galadini et al., 2000). To improve and implement the database, published complementary studies were selected for some specific areas considered to not be exhaustively covered by the aforementioned collection of faults, including Sardinia, SW Alps, Tuscany, the Adriatic front, Puglia, and the Calabrian Arc. For these areas, faults were selected on the grounds of scientific contributions that documented recent fault activity based on seismic, field, and paleoseismological data. In particular, for the southern Sardinia, the fault pattern proposed by Casula et al. (2001) was used. For the SW Alps, the works of Augliera et al. (1994), Courboulex et al. (1998), Larroque et al. (2001), Christophe et al. (2012), Sue et al. (2007), Capponi et al. (2009), Turino et al. (2009) and Sanchez et al. (2010) were followed. For the Tuscany area, Brogi et al. (2003), Brogi et al. (2005), Brogi (2006), Brogi (2008), Brogi (2011), and Brogi and Fabbrini (2009) were consulted. For the buried northern Apennines and Adriatic front, the fault datasets provided by Scrocca (2006), Cuffaro et al. (2010), and Fantoni and Franciosi (2010) were used. For the Puglia region, data from Patacca and Scandone (2004) and Del Gaudio et al. (2007) were used, while for the Calabrian Arc data were obtained from Polonia et al. (2016).Seismicity: to obtain a complete earthquake catalogue for the Italian territory, the following catalogues of instrumental and historical seismicity were integrated: (1) the CSI1.1 database (http://csi.rm.ingv.it; Castello et al., 2006) for the period 1981–2002, (2) the ISIDe database (http://iside.rm.ingv.it/iside/; IsideWorkingGroup, 2016) for the period 2003–2017 (Figure 3) and the CPTI15 (https://emidius.mi.ingv.it/CPTI15-DBMI15/; Rovida et al., 2016) for the period 1000-1981.The CSI 1.1 database (Castello et al., 2006) is a relocated catalogue of Italian earthquakes during the period 1997–2002. This collection derives from the work of Chiarabba et al. (2005). Most seismic events are lower than 4.0 in magnitude and are mostly located in the upper 12 km of the crust. A few earthquakes exceed magnitude 5.0, and the largest event is Mw 6.0. Due to their poorly constrained location, events with Mw < 2.0 were removed.The ISIDe database (IsideWorkingGroup, 2016) provides the parameters of earthquakes obtained by integrating data from real time and Italian Seismic Bulletin earthquakes. The time-span of this compilation begins in 1985. To avoid an overlap with the CSI database, only the time interval 2003–2017 was considered. Mw = 2.0 is the lower limit used for earthquake magnitude. The CPTI15 database integrates the italian macroseismic database version 2015 (DBMI15, Locati et al., 2016) and instrumental data from 26 different catalogues, databases and regional studies starting from the 1000 up to the 2014. To avoid overlapping of data with the utilized instrumental datasets, from the CPTI2015 we took data for the period 1000-1981 in the range of Mw 4-7.Method: starting from the entire faults dataset, the length of each structure was calculated (Lf, in km). Then, the Italian territory was divided into a grid with square cells of 25 x 25 km. The length of the longest fault crossing each cell characterizes the parameter “fault length” (Lf) of the considered cell. In the second step, these lengths were used as the input parameter to empirically derive the magnitude. The equations provided by Leonard (2010), were applied for earthquake magnitude-fault length relationships to infer the Potential Expected Maximum Magnitude as M = a + b ∗ log (Lf), with a=4.24 and b=1.67. The obtained magnitudes were assigned to each single cell. Furthermore, the maximum magnitude recorded/reported in instrumental/historical catalogs is associated to each containing cell.The resulting datasets are presented in txt format and included in the following files:- Grid_Coordinates.txt (contains ID and coordinates of grid's elements)- Grid_Structure.txt (contains geometry and specifications of the used grid)- Table_results (five columns table containing 1=element ID, 2= element max fault length (Lf_max in km), 3=element max Mw from instrumental record (MwInstr_max), 4=element max Mw from historical record (MwHist_max), 5=element max Mw derived by empirical relationship (PEMM).- The full list of references is included in the file Petricca_2018-003_References.txt
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.
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 open access database compiles stress magnitude information from various sources. It currently includes 568 data records in the area of Germany and adjacent regions (latitude: 47 - 55.5 N; longitude: 5.8 - 15.1 E). The data records are ranked after a newly developed quality scheme for stress magnitude data. The data are provided in two formats: Excel-file (stressmagdata_germany_2020.xlsx), comma separated fields (stressmagdata_germany_2020.csv). Additional files include a) an overview over the compiled parameters including the abbreviation keys for stress magnitude indicators and stress regimes (List_of_parameters.pdf); b) the key for the referenced data sources (Key_for_ref_labels.pdf); and c) the applied quality ranking scheme (Quality_ranking_scheme.pdf).
The Atacama Fault System (AFS) in N-Chile is a complex fault system with a variety of fault segments showing different degrees of activity. Initiated as a trench-linked fault system during the Jurassic it is now exposed in the Coastal Cordillera in the forearc of the Nazca-South America convergent plate margin. Fault scarps and surface ruptures indicate varying degrees of reactivation of this fault system that most likely roots into the subduction zone interface at the downdip end of coupling. Therefore, the interaction of these two systems is evident though not well understood. The active fault database for the northernmost segment of the Atacama Fault System (AFS) is the result of creating a comprehensive catalogue of active faults in the forearc to investigate activity patterns of the forearc in relation with megathrust segmentation and upper plate seismicity in the Coastal Cordillera of N-Chile (19°12’S - 25°12’S). The dataset has been compiled in Arc-GIS and is available as .mpk as well as .kmz formats to be visualised in Google Earth. The activity patterns are mapped according to a well-defined set of criteria (see below). The database for activity starts out from a thorough literature review and is supplemented by new evidences combining interpretation of remote sensing data, field work and upper plate seismicity from the Integrated Plate Boundary Observatory in Chile (IPOC) (Sippl et al., 2018) and a local seismic catalogues covering the area of the Salar Grande segment (Bloch et al., 2014). It also includes the available age data of offset geological units as references to bracket the chronology of fault activity. Fault activity for this study has been defined according to the Quaternary fault and fold database of the United States (https://www.usgs.gov/natural-hazards/earthquake-hazards/faults?qt-science_support_page_related_con=4#qt-science_support_page_related_con), but is subject to significant error due to slow slip rates (< 0.2mm/yr), few chronologically constrained fault offsets and lack of historically or instrumentally observed earthquakes along the fault segments. Therefore, this database does not have the aim to serve as active fault database for seismic hazard assessment. It has been created with the clear aim to serve as database for general aspects of upper plate fault reactivation in relation with the megathrust seismic cycle and megathrust segmentation. This publication is part of an ongoing study investigating the interaction of megathrust segmentation with activity patterns in the overriding forearc.
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
The Pamir plateau protrudes ~300 km between the Tajik- and Tarim-basinlithosphere of Central Asia. We present a new local-seismicity catalog, a focal-mechanism catalog, and a P-wave velocity model of the of the collision system between the Pamir plateau and the Tarim basin. The data suggest a south-dipping Asian slab that overturns in its easternmost segment. The largest principal stress at depth acts normal on the slab and is orientated parallel to the plate convergence direction. In front (south) of the Asian slab, a volume of mantle with elevated velocities and lined by weak seismicity constitutes the postulated Indian mantle indenter. The data set consists of an earthquake catalog, an earthquake focal mechanism catalog and a subsurface P-wave velocity model of the central and eastern Pamir plateau and the adjacent north-western Tarim basin; between 36.8–40.0 °N and 72.2–78.0 °E. It was collected to identify the deep tectonic structures that determine the lithospheric architecture of the Pamir plateau. Earthquakes were recorded by two temporary seismic deployments. Earthquakes that occurred between 1st August 2008 and 6th June 2010 were primarily recorded by the TIPAGE network (Yuan et al., 2008); those, between 3rd August 2015 and 23rd June 2017 by the East Pamir and Sarez aftershock networks (Yuan et al., 2018a, b). The earthquake catalog contains 1,493 seismic events at depth >50 km. They were localized in the present 3-D velocity model. Some events were re-located with hypoDD. The focal mechanism catalog consists of double-couple fault-slip parameters for 38 events, 29 of which are newly determined using the HASH algorithm and 9 are moment tensors from Kufner et al. (2016). The P wave-velocity model has been determined using simulps from 2,264 seismic events with well-constrained P- and S-wave arrivals. It is parameterized as velocity gradients between nodes with a horizontal and vertical spacing of 40 and 15 km, respectively. Unresolved nodes were masked using a checkerboard resolution test. The full description of the methods is provided in the data description file.
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