Other language confidence: 0.987583499504168
The file corresponds to a code written using the R software version 4.0.5 (R Core Team, 2021). We used a Bayesian robust regression to predict the posterior probability P(L) at which a given location yi in our study areas (north Patagonia, Chile) is classified as part of a landslide source, transport, or deposition area. We used the NUTS sampling scheme implemented in the STAN probabilistic programming language (Carpenter et al., 2017) to draw samples from the joint posterior distribution via the R package brms (Bürkner, 2017). We ran four independent Hamiltonian Monte Carlo chains based on 2000 iterations including 500 warm-up samples and checked each chain for convergence. We assessed the performance of this classifier based on its posterior predictive distribution and recorded the fraction of correct classifications compared to the observed frequency of landslides in all study areas and for all landform types. We find that higher crown openness and wind speeds credibly predict higher probabilities of detecting landslides regardless of topographic location, though much better in low-order channels and on midslope locations than on open slopes. Wind speed has less predictive power in areas that were impacted by tephra fall from recent volcanic eruptions, while the influence of forest cover in terms of crown openness remains.
The model contains the 3D structure of Vp and Vs in the crust and the mantle under the European Alps, as published in Kästle et al. (2025). It is the result of a direct inversion of surface-wave data, from ambient noise and earthquake records, and of teleseismic P and S wave data. A Bayesian tomography approach is used where we implement a reversible jump Markov chain Monte Carlo method to constrain the free parameters. This gives not only the mean Vp and Vs values, but also their uncertainties, as well as a distribution (histograms) of the sampled velocity parameters at each point of the model.
We provide the model results of the manuscript "Glacial-isostatic adjustment models using geodynamically constrained 3D Earth structures" (Bagge et al. 2020, Paper) including the (1) predicted relative sea-level and (2) applied sea-level data. The predicted relative-sea level is calculated with the VIscoelastic Lithosphere and MAntle model VILMA (Klemann et al. 2008, 2015, Martinec et al. 2018, Hagedoorn et al. 2007, Martinec & Hagedoorn 2005, Kendall et al. 2005). The glacial-isostatic adjustment models uses different Earth structures (3D, 1D global mean and 1D regionally adapted; Bagge et al. 2020, Paper; Bagge et al. 2020, https://doi.org/10.5880/GFZ.1.3.2020.004) and ice histories (ICE-5G, Peltier 2004; ICE-6G, Peltier et al. 2015, Argus et al. 2014; NAICE, Gowan et al. 2016) resulting in 44 3D models, 54 1D global mean models and 162 1D regionally adapted models. For more information on model description and input data see Bagge et al. (2020, Paper) and Bagge at al. (2020, https://doi.org/10.5880/GFZ.1.3.2020.004). The provided output data include (1a) the global distribution of predicted relative-sea level at 14 kilo years before present as ensemble range of the 3D GIA models for three ice histories as netCDF files, (1b) the predicted relative-sea level at eight locations at 14 kilo years before present for all models as ASCII file and (1c) the predicted relative sea-level for the deglaciation period for all models as ASCII files. Eight locations include Churchill, Angermanland, Ross Sea (Antarctica), San Jorge Gulf (Patagonia), Central Oregon Coast, Rao-Gandon Area (Senegal), Singapore and Pioneer Bay (Queensland, Australia). (2) The about 520 applied sea-level data provide information on time, relative sea-level and type of sea-level data. They are extracted for the eight locations from the GFZ database using SLIVisu (Unger et al. 2012, 2018) and provided as ACSII files.
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
The REHEATFUNQ Python package helps to work with the (residual) scatter of surface heat flow even in small regions. REHEATFUNQ uses a stochastic model for regional aggregate heat flow distributions (RAHFD), that is, the collected set of heat flow measurements within a region marginalized to the heat flow dimension. The stochastic model is used in a Bayesian analysis that (1) yields a posterior estimate of the RAHFD which captures the range of heat flow within the analysis region, and (2) quantifies the magnitude of a surface heat flow anomaly within the region, for instance through the generating frictional power. The stochastic model underlying REHEATFUNQ views heat flow data, uniformly sampled across the region of interest, as a random variable. A gamma distribution is used as a model for this random variable and information from the global data set of Lucazeau (2019) is introduced by means of a conjugate prior (Miller, 1980). The detailed science behind the model is described in Ziebarth et al. (202X). The analysis by Ziebarth et al. (202X) can be reproduced through the Jupyter notebooks contained in the subdirectory “jupyter/REHEATFUNQ/”. The location specified in the map below covers the region to which REHEAFUNQ is applied in this analysis. REHEATFUNQ is a Python package that uses a compiled Cython/C++ backend. Compiling REHEATFUNQ requires the Meson build system and a number of scientific libraries and Python packages (and their dependencies) that are listed in the documentation. A Docker image “reheatfunq” is provided as an alternative means of installation. The Docker image comes in two flavors, specified in “Dockerfile” and “Dockerfile-stable”. The former is based on the current “python:slim” image and downloads further dependencies through the Debian package manager, leading to a short image generation time. The latter bootstraps the REHEATFUNQ dependencies from source, aiming to create a reproducible model. To do so, “Dockerfile-stable” depends on the sources contained in “vendor-1.3.3.tar.xz”. If you plan to build the stable image, download both “REHEATFUNQ-1.3.3.tar.gz” and “vendor-1.3.3.tar.xz”, and see the README contained in the latter. Later versions of the “REHEATFUNQ” archive are compatible with the latest “vendor” archive. A quickstart introduction and the API documentation can be found in the linked documentation.
We provide a set of grid files that collectively allow recreating a 3D geological model which covers the Central European Basin System and adjacent areas. The data publication is a complement to the publication of Maystrenko and Scheck-Wenderoth (2013) with a higher spatial and stratigraphic resolution. The structural model consists of (i) 11 sedimentary units including sea water; (ii) five crystalline crust units composed of four upper crustal units and one lower crustal unit; (iii) one lithospheric mantle unit. The available files include information on the regional variation of these geological units in terms of their depth and thickness, both attributes being allocated to regularly spaced grid nodes with horizontal spacing of 4 km. In comparison, the horizontal spacing of data provided by Maystrenko and Scheck-Wenderoth (2013) was 16 km. Besides, the model provided here resolves Permian, Mesozoic and Cenozoic sediments and Permo-Carboniferous volcanics. The model has originally been developed to analyse the first-order structural features characterizing the crust and the lithospheric mantle below the Central European Basin System and adjacent areas and obtain a basis for numerical simulations of heat transport and to calculate the lithospheric-scale conductive thermal field. Such simulations require the subsurface variation of physical rock properties to be defined, the 3D model differentiates units of contrasting materials, i.e. rock types. On that account, a large number of geological and geophysical data have been analysed (see Related Works) and we shortly describe here how they have been integrated into a consistent 3D model (Methods). For further information on the data usage and the characteristics of the units (e.g., lithology, density, thermal properties), the reader is referred to Maystrenko and Scheck-Wenderoth (2013). The contents and structure of the grid files provided herewith are described in the Technical Information section and the associated data description file (pdf).
The Central Andes (~21°S) is a subduction-type orogeny formed in the last ~50 Ma from the subduction of the Nazca oceanic plate beneath the South American continental plate. However, the most important phases of deformation occur in the last 20 Ma. Pulses of shortening have led to the sudden growth of the by the Altiplano-Puna plateau. Previous studies have provided insights on the importance of various mechanisms on the overall shortening such as the weakening of the overriding plate from crustal eclogitization and delamination, or the importance of a relatively high friction at the subduction interface, and weak sediments in foreland. However none of them has addressed the mechanism behind these shortening pulses yet. Therefore, we built a series of high resolution 2D visco-plastic subduction models using the ASPECT geodynamic code, in which the oceanic plate is buoyancy-driven and the velocity of the continent is prescribed. We have also implemented a realistic geometry for the south American plate at ~30 Ma. We propose a new plausible mechanism (buckling and steepening of the slab) as the cause of these pulses. The buckling leads to the blockage of the trench. Consequently, the difference of velocity between the South American plate and the trench is accommodated by shortening. The data presented here includes the parameters files, for the reference model (S1) and the following alternative simulations: models with variation of the friction at the subduction interface (S2a-c), a model without eclogitization of the lower crust (S3) and a model with higher thermal conductivity of the upper crust (S4). Additionally, this publication includes the initial composition and thermal state of the lithosphere used for the models and a Readme file that gives all the instructions to run them.
The Central Andean orogeny is caused by the subduction of the Nazca oceanic plate beneath the South-American continental plate. In Particular, the Southern Central Andes (SCA, 27°-40°S) are characterized by a strong N-S and E-W variation in the crustal deformation style and intensity. Despite being the surface geology relatively well known, the information on the deep structure of the upper plate in terms of its thickness and density configurations is still scarcely constrained. Previous seismic studies have focused on the crustal structure of the northern part of the SCA (~27°-33°S) based upon 2D cross-sections, while 3D crustal models centred on the South-American or the Nazca Plate have been published with lower resolution. To gain insight into the present-day state of the lithosphere in the area, we derived a 3D model that is consistent with both the available geological and seismic data and with the observed gravity field. The model consists on a continental plate with sediments, a two-layer crust and the lithospheric mantle being subducted by an oceanic plate. The model extension covers an area of 700 km x 1100 km, including the orogen, the forearc and the forelands.
The software RST Evaluation is a series of scripts to semi-automatically evaluate shear experiments done at the Helmholtz Laboratory for Tectonic Modelling. In principle, it may be used for other measurements done in a similar setup, but it was build with our standardized workflow in mind. The shear experiments are done in a ring shear type shear cell rst.pc01 manufactured by D. Schulze (Details in ASTM standard D-6773). It uses an easy and reproducible workflow to determine yield properties, cohesion and dilational properties of a granular bulk material, such as sand or glass beads.
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