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PebbleCounts: a Python grain-sizing algorithm for gravel-bed river imagery

Grain-size distributions and their associated percentiles are a key geomorphic metric of gravel-bed rivers. Traditional measurement methods include manual counting or photo sieving, but these are typically achievable only at the patch (1 square meter) scale. With the advent of unmanned aerial vehicle systems and increasingly high-resolution cameras, we can now generate orthoimagery over large areas at resolutions of <1 cm. These scales, along with the complexity of many natural environments in high-mountain rivers, necessitate different approaches for photo sieving. Here, a new open-source algorithm is presented: PebbleCounts. As opposed to other image segmentation methods that use a watershed approach and automatically segment entire images, PebbleCounts relies on k-means clustering in the spatial and spectral (color) domain and rapid manual selection of well-outlined grains. This results in improved estimates for complex river-bed imagery without the need for post-processing.

DAS Convert - Convert distributed acoustic sensing data

Convert and downsample distribute acoustic sensing (DAS) data acquired by Silixa iDAS or ASN OptoDAS to seismological data formats. Main purpose is to quickly convert and downsample massive amounts of high-resolution DAS data to MiniSEED and other seismological data formats. To handle the massive amount of data generated by DAS interrogators, the conversion tool is leveraging parallel I/O and multi-threaded signal-processing. A high throughput can be archived while converting and downsampling data in parallel threads. The tool can interact with tape storage systems and messaging bots to monitor the conversion process. The signal processing routines are based on Pyrocko, a mature and well tested seismological framework.

Ring-shear test data of feldspar sand FS900S used in the Tectonic Modelling Laboratory at the University of Bern (Switzerland)

This dataset provides friction data from ring-shear tests on feldspar sand FS900S used for the simulation of brittle behaviour in crust- and lithosphere-scale analogue experiments at the Tectonic Modelling Laboratory of the University of Bern (Zwaan et al. in prep; Richetti et al. in prep). The materials have been characterized by means of internal friction parameters as a remote service by the Helmholtz Laboratory for Tectonic Modelling (HelTec) at the GFZ German Research Centre for Geosciences in Potsdam (Germany). According to our analysis both materials show a Mohr-Coulomb behaviour characterized by a linear failure envelope. Peak, dynamic and reactivation friction coefficients of the feldspar sand are μP = 0.65, μD = 0.57, and μR = 0.62, respectively, and the Cohesion of the feldspar sand is in the order of 5-20 Pa. An insignificant rate-weakening of less than 1% per ten-fold rate change is registered for the feldspar sand. Granular healing is also minor.

Rheometric Analysis of Viscous Material Mixtures Used in the Tectonic Laboratory (TecLab) at Utrecht University, Netherlands

This dataset contains measurements of viscous and viscoelastic materials that are used for analogue modelling. Proper density and viscosity scaling of ductile layers in the crust and lithosphere, requires materials like Polydimethylsiloxane (PDMS), to be mixed with fillers and low viscoity silicone oils. Changing the filler content and filler material, the density, viscosity and power-law coefficient can be tuned according to the requirements. All materials contain a large amount of PDMS and all but one a small amount of an additional silicone oil. Adding plasticine or barium sulfate lead to shear thinning rheologies with power-law exponents of p<0.95. Adding corundum powder only has a minor effect on the power-law exponent. Some mixtures also have an apparent yield point but all are in the liquid state in the tested range. In general, the rheologies of the materials are very complex and in some cases strongly temperature dependent. However, in the narrow range of relevant strain rates, the behaviour is well defined by a power-law relation and thus found suitable for simulating ductile layers in crust and lithosphere.

Datasets of analog modeling results for the V-shaped opening of the South China Sea: 3D DEMs and PIV results

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.

geogravL3 - a Python Package for Processing Earth Gravity Field Data

This package processes Earth gravity field data—provided as spherical harmonic coefficients—into gridded, domain-specific datasets. It also includes uncertainty estimation and the generation of regional mean time series.

Rheology of viscous materials from the CNR-IGG Tectonic Modelling Laboratory at the University of Florence (Italy)

This dataset provides rheometric data of three viscous materials used for centrifuge experiments at the Tectonic Modelling Laboratory of CNR-IGG at the Earth Sciences Department of the University of Florence (Italy). The first material, PP45, is a mixture of a silicone (Polydimethylsiloxane or PDMS SGM36) and plasticine (Giotto Pongo). The PDMS is produced by Dow Corning and its characteristics are described by e.g. Rudolf et al. 2016a,b). Giotto Pongo is produced by FILA (Italy). Both components are mixed following a weight ratio of 100:45, and the final mixture has a density of 1520 kg m3. The second material, SCA705 is a mixture of Dow Corning 3179 putty, mixed with fine corundum sand and oleic acid with a weight ratio of 100:70:05 and a resulting density of 1660 kg m3. The final material, SCA7020 consists of the same components as SCA705, but with a slightly higher oleic acid content reflected in the weight ratio of 100:70:20. The mixture’s density is 1620 kg m3. The material samples have been analyzed in the Helmholtz Laboratory for Tectonic Modelling (HelTec) at GFZ German Research Centre for Geosciences in Potsdam using an Anton Paar Physica MCR 301 rheometer in a plate-plate configuration at room temperature (20˚C). Rotational (controlled shear rate) tests with shear rates varying from 10-4 to 1 s-1 were performed. Additional temperature tests were run with shear rates between 10-2 to 10-1 s-1 for a temperature range between 15 and 30˚C. According to our rheometric analysis, the materials all exhibit shear thinning behavior, with high power law exponents (n-number) for strain rates below 10-2s-1, while power law exponents are lower above that threshold.For PP45, the respective n-numbers are 4.8 and 2.6, for SCA705 6.7 and 1.5, and for SCA7020 9.1 and 2.0. The temperature tests show decreasing viscosities with increasing temperatures with rates of -3.8, -1.4 and -1.9% per ˚K for PP45, SCA705 and SCA7020, respectively. An application of the materials tested can be found in Zwaan et al. (2020).

TEAM – The Transformer Earthquake Alerting Model

TEAM, the Transformer Earthquake Alerting Model is a deep learning model for real time estimation of peak ground acceleration (TEAM), earthquake magnitude and earthquake location (TEAM-LM). This software package contains the joint implementation of both TEAM and the derivative TEAM-ML, as well as the scripts for training and evaluating these models. In addition, it contains scripts to download an early warning datasets for Japan and implementations of baseline approaches for the estimation of earthquake magnitude and peak ground acceleration. TEAM is implemented in Python. TEAM and TEAM-ML have a variety of configuration parameters that are documented in the README. These configurations need to be provided in JSON format. In addition, multiple example configuration files are provided in the subdirectories pga_configs and magloc_configs. Please note that this implementation is intended for research purpose only. Production use is discouraged.

gravitools - A collection of tools to analyze gravimeter data

This Python package is a collaborative effort by the gravity Metrology group at the German Federal Agency for Carthography and Geoesy (BKG) and the Hydrology section at GFZ Helmholtz Centre for Geosciences. It comprises functionalities and features around the respectively new instrument type of a Quantum Gravimeter (here AQG). New (standardized) instrument data format additional to new measurement and processing concepts lead to the first collection of scripts and now complete python package for a fully-featured analysis of AQG data. This encompasses live-monitoring while the instrument is actually measuring (with enhanced functionality than what is provided by the manufacturer), data processing, visualizations as well as archiving data, fulfilling the idea of reproducible data within FAIR principles. Many of these functionalities and concepts also apply to other gravimeter types. It is thus planned to include also access and processing of data for these other devices (starting in the near future with CG-6 relative gravimeters). This package is actively maintained and developed. If you are interested in contributing, please do not hesitate to contact us. Please find instructions for its installation and usage in the documentation or git repository, linked in the left panel. gravitools is listed in the python standard repository database "PyPi". Some highlight features, available in the first official stable release are: • Read and process raw data of the Exail Absolute Quantum Gravimeter (AQG) • Apply standardized or customized AQG data processing and outlier detection • Read and write processed datasets with metadata to .nc-files in NETCDF4-format • Handle Earth orientation parameters (EOP) from iers.org for polar motion correction • Visualize data with matplotlib • CLI for standard processing of AQG raw data to .nc-file • Dashboard for real-time processing and visualization during measurements (on AQG laptop) • Dashboard includes a proposed standard template for a measurement protocol • Standardized, easy-to-read and modify config files for processing options and reproducible data handling • Generation of PDF reports from individual measurements

Software for analyzing Globe at Night data

This publication contains software that can be used to pre-process data from the Globe at Night citizen science project, and then run an analysis to determine the rate of change in sky brightness. The software requires input data, which can be obtained directly from Globe at Night. The data used for our publication "Citizen scientists report global rapid reductions in the visibility of stars from 2011 to 2022" is published here, and can be used as input to the software. The process requires access to the World Atlas of Artificial Night Sky Brightness, which is also available from GFZ Data Services.

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