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A Database of Centrifuge Analogue Models Testing the Influence of Pre-Existing Weak Zones During Continental Compression

This dataset presents the raw data of an experimental series of centrifuge models performed to test the influence of pre-existing weak zones in the lower crust (herein after referred to as Weak Lower Crust –WLC) during continental compression. We varied the width of the WLC, the dip of the interfaces bounding the WLC and the frictional properties at the WLC-LC interface by using lubricant (vaseline). In this dataset, we provide four different types of data, that can serve as supporting material and can be used for further analysis: 1) The top-view photos, taken at different stages and showing the deformation process of each model; 2) Digital Elevation Models (DEMs) used to reconstruct the 3D deformation of the performed analogue models; 3) Line-drawing of fault and fracture patterns to be used for fault statistical quantification; 4) A Python script to draw swath profiles (outputs) of the analogue models. Further details on the modelling strategy can be found in the publication associated with this dataset and in Milazzo et al. (2021), using a similar setup for achieving compression in the centrifuge. Materials used for these analogue models were described in Corti (2012), Montanari et al. (2017), Del Ventisette et al. (2019), Zou et al. (2024) and Wan et al. (2025).

Drone based photogrammetry data at the Geysir geothermal field, Iceland

Geysers are localized hydrothermal vents that periodically erupt with gas bubbles at the surface. Understanding their distribution, dynamics, and conduit geometry is critical to understand the fluid and heat transfer through the crust. To explore this at the Geysir geothermal field in Iceland, we analyzed the spatial distribution of thermal features using high-resolution UAV-based optical and infrared cameras. Based on this, Walter et al. (2020) identified 364 distinct thermal spots. Here we release the high-resolution drone orthomosaic dataset at the Geysir geothermal field, Iceland.

GDEMM2024: 30 Arcsec Global Digital Elevation Merged Model 2024, a suite for Earth relief

We merged various digital elevation models (DEMs) published in the recent years and created an up-to-date composite and global solution for Earth’s topography and bathymetry. Compared to the original geographically limited data sets, the final product is a seamless merged grid which additionally provides high resolution and accuracy topography and depth globally. We provide Earth relief grids w.r.t EIGEN-6C4 global geoid in terms of surface and bedrock elevation, ice thickness, and land-type masks which have been substantially improved w.r.t the global grids found in literature. We assessed the quality of the merged surface elevations w.r.t the heights given for about globally distributed 5000 ITRF stations. The merged surface model shows improvement of a factor of three w.r.t the other commonly used DEMs in terms of standard deviation. In addition to the four grids, GDEMM2024_SUR, GDEMM2024_BED, GDEMM2024_ICE, and GDEMM2024_LTM, we provide two additional files, the surface elevation without water (GDEMM2024_TBI) and the GDEMM2024_GEO file to transform the heights above EIGEN_6C4 geoid to ellipsoidal heights. The final grids are provided both in 30 arcsec and 1arcmin resolution and in GeoTIFF format which is one of the standards that is available in GMT (Generic Mapping Tools), GDAL (Geospatial Data Abstraction Library) and in almost all GIS software systems.

High-resolution photogrammetry data of the Santiaguito lava dome collected by UAS surveys

Imaging growing lava domes has remained a great challenge in volcanology due to their inaccessibility and the severe hazard of collapse or explosion. Here, we present orthophotos and topography data derived from a series of repeated survey flights with both optical and thermal cameras at the Caliente lava dome, part of the Santiaguito complex at Santa Maria volcano, Guatemala, using an Unoccupied Aircraft System (UAS). The data archived here supplements the material detailed in Zorn et al. (2020, https://doi.org/10.1038/s41598-020-65386-2). Note, all files are saved in WGS 84 / UTM Zone 15N format. The data are provided the following .zip folders: - 2020-001_Zorn-et-al_DEM-Geotiffs-zip: DEMs of surveys A-D in geotiff format (.tif) - 2020-001_Zorn-et-al_Orthophotos.zip: Orthophotos of surveys A-D and 2 thermal surveys as Tiff-images (.tif). A .jpg of the color scale for the thermal data is also included - 2020-001_Zorn-et-al_Point_Cloud_Models.zip: Point clouds of surveys A-D, 2 thermal surveys (.las)

A database of R-R-R triple junction analogue and numerical models

This dataset presents the raw data from two experimental series of analogue models and four numerical models performed to investigate Rift-Rift-Rift triple junction dynamics, supporting the modelling results described in the submitted paper. Numerical models were run in order to support the outcomes obtained from the analogue models. Our experimental series tested the case of a totally symmetric RRR junction (with rift branch angles trending at 120° and direction of stretching similarly trending at 120°; SY Series) or a less symmetric triple junction (with rift branches trending at 120° but with one of these experiencing orthogonal extension; OR Series), and testing the role of a single or two phases of extension coupled with effect of differential velocities between the three moving plates. An overview of the performed analogue and numerical models is provided in Table 1. Analogue models have been analysed quantitatively by means of photogrammetric reconstruction of Digital Elevation Model (DEM) used for 3D quantification of the deformation, and top-view photo analysis for qualitative descriptions. The analogue materials used in the setup of these models are described in Montanari et al. (2017), Del Ventisette et al. (2019) and Maestrelli et al. (2020). Numerical models were run with the finite element software ASPECT (e.g., Kronbichler et al., 2012; Heister et al., 2017; Rose et al., 2017).

river-clusters: Clustering river profiles from topographic data

This software package contains code for performing agglomerative hierarchical clustering on river long profiles extracted from topographic data. The software requires initial topographic analysis to extract river profiles based on the Edinburgh Land Surface Topographic Tools package. Detailed documentation and tutorials for installation and running the code can be found at https://lsdtopotools.github.io/LSDTT_documentation/. The package written in Python and based on the scipy cluster package. The development version of the code can be found on GitHub (https://github.com/UP-RS-ESP/river-clusters) along with full instructions on how to install and run the code.

High resolution Digital Elevation Model of Merapi summit in 2015 generated by UAVs and TLS and TanDEM-X

This data is an high resolution Digital Elevation Model (DEM) generated for the Merapi summit by combining terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) photogrammetry data and TanDEM-X data acquired in the years between 2012 and 2017. The structures of the data are further analysed in Darmawan et al. 2017a (http://doi.org/10.1016/j.jvolgeores.2017.11.006), and a previous DEM was available in Darmawan et al. 2017b (https://doi.org/10.5880/GFZ.2.1.2017.003). The 3D point clouds of the different data were merged and interpolated to a raster format (Geotiff format).

Drainage divide networks, Part 2: Response to perturbations - Movie files

The movies in this dataset are supplementary to the article of Scherler and Schwanghart (submitted), in which experiments with numerical landscape evolution models have been conducted to analyze the evolution of drainage divide networks. The experiments were run in MATLAB with the TopoToolbox landscape evolution model (TTLEM) 1.0 (Campforts et al., 2017), and analyzed with the TopoToolbox v2 (Schwanghart and Scherler, 2014). The different experiments in this dataset comprise five different setups, called ‘Initialize’, ‘Reference’, ‘Rotate’, ‘Inclined’, and ‘Spheres’, which all simulate the evolution of landscapes over 10 Million years. See Scherler and Schwanghart (submitted) for details on the different models. For each model run, we produced five different movies that were saved as Audio Video Interleave (AVI) files. All movies show the evolution of the topography and the drainage divide network, colored for different properties. Detailed description of the files is provided in the associated data description.

High resolution Digital Elevation Model of Merapi summit in 2015 generated by UAVs and TLS

This data publication is a high resolution Digital Elevation Model (DEM) generated for the Merapi summit by combining terrestrial laser scanning (TLS) and unmanned aerial vehicles (UAVs) photogrammetry data acquired in 2014 and 2015, respectively. The structures of the data are further analysed in Darmawan et al. 2017 (http://doi.org/10.1016/j.jvolgeores.2017.11.006). The published datasets consist of combined point clouds with ~65 million data points and a DEM with a resampled resolution of 0.5 m. The DEM data covers the complexity of the Merapi summit with area of 2 km2. The coordinate of the datasets is projected to global coordinates (WGS 1984 UTM Zone 49 South). TLS is a topography mapping technique which exploits the travel time of a laser beam to measure the range between the ground-based scanning instrument and the earth’s surface. TLS provides high accuracy, precision, and resolution for topography mapping, however, it requires different scan position to obtain accurate topography model in a complex topography. The TLS dataset was acquired by using a long-range RIEGL VZ-6000 instrument with a Pulse Repetition Rate (PRR) of 30 kHz. The Merapi data includes an observation range of 0.129 – 4393.75 m, a theta range (vertical) of 73 – 120° with a sampling angle of 0.041°, a phi range (horizontal) of 33° - 233° with a sampling angle of 0.05°, and 12 reflectors for each scan. The used TLS dataset was achieved by combining two scan positions, both realized in September 2014. In order to reduce still eminent shadowing, we conducted additionally a UAV photogrammetry survey. The UAV data allows to fill data gaps and generate a complete 3D point cloud. The UAV photogrammetry was conducted by using DJI Phantom 2 quadcopter drone in October 2015. The drone carried GoPro HERO 3+ camera and a H3-3D gimbal to reduce image shaking. We obtained over 300 images which cover the summit area of Merapi. By applying the Structure from Motion algorithm, we are able to generate a 3D point cloud model of Merapi summit. Further details on this procedure are provided in Darmawan et al. (2017). Structure from Motion is a technique to generate a 3D model based on 2D overlapped images. The algorithm detects and matches the same ground features of 2D images, reconstructs a 3D scene, and calculates a depth map for each camera frame. The algorithm used is implemented in Agisoft Photoscan Professional software. After importing the images in Agisoft, we used the ‘align image’ function with high accuracy setting to generate 3D sparse point cloud and ‘build dense cloud’ function with high quality to generate 3D dense point cloud. The 3D point clouds of TLS and UAV photogrammetry were then georeferenced to our georeferenced 3D point cloud which acquired in 2012. The RMS of TLS and UAV photogrammetry during georeferenced is 0.60 and 0.44 m, respectively, as described in Further details on this procedure are provided in Darmawan et al. (2017). After georeferencing, both 3D point clouds were merged and interpolated to a raster format in the ArcMap software.

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