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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
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).
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.
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.
This data publication contains airborne wind and eddy covariance data files, that were recorded with the ASK-16, a motorized glider owned by the FU Berlin, Germany. These data files include a large range of meteorological variables (wind speed, direction, temperature, humidity, etc.), positioning information, but also information on atmospheric chemistry (mainly methane concentration, carbon dioxide concentration, water vapor concentration) and turbulent matter (CH4 and CO2) and energy fluxes (latent heat flux) is available. Measurements were recorded between 2017 and 2022 to: (1) obtain three-dimensional wind vectors in within the atmospheric boundary layer (2) calibrate of wind measurements (3) record turbulent energy and matter fluxes A lot of these data files have been used in the publication “The ASK-16 Motorized Glider: An Airborne Eddy Covariance Platform to measure Turbulence, Energy and Matter Fluxes (to be published in atmospheric measurement techniques)” by Wiekenkamp et al., 2024a. This publication also provides a lot of additional details on the measurement system, the data handling and processing.
In the last years, a whole series of codes has been developed to process airborne wind data. Initially, the PyWingpod package was mainly build to handle data from the Wingpod of the ASK-16 motorized glider of the FU Berlin. However, due to the modular buildup of the package, functions within the different libraries can also be used to process data from other airborne platforms. Functions and scripts within PyWingpod have been developed to: a. load and process airborne five hole probe and meteo data, this includes (1) 5 hole probe pressure sensor data (static pressure, dynamic pressure and the differential alpha and beta pressure), (2) INS-GNSS data, (3) Temperature and humidity data and (4) any auxillary data that you want to add to the time series/ data frame. b. calibrate pressure sensor data from the five hole probe (mainly to correct for any effect of aircraft movement) c. calculate a reliable wind vector based on the available data that are specified in a. and the calibration parameters, which are obtained in step b.
Monitoring Velocity Changes using Ambient Seismic Noise SeisMIC (Seismological Monitoring using Interferometric Concepts) is a python software that emerged from the miic library. SeisMIC provides functionality to apply some concepts of seismic interferometry to different data of elastic waves. Its main use case is the monitoring of temporal changes in a mediums Green's Function (i.e., monitoring of temporal velocity changes). SeisMIC will handle the whole workflow to create velocity-change time-series including: Downloading raw data, Adaptable preprocessing of the waveform data, Computating cross- and/or autocorrelation, Plotting tools for correlations, Database management of ambient seismic noise correlations, Adaptable postprocessing of correlations, Computation of velocity change (dv/v) time series, postprocessing of dv/v time series, plotting of dv/v time-series
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.
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.
This data set provides a series of experiments from ring-shear tests (RST) on various materials that are used at several laboratories worldwide. The data contains the results of slide-hold-slide tests and the processed outputs of standardized ring shear tester data from related publications. Additionally, microscopy images of the materials under plain and polarized light are provided. The time dependent restrengthening of the materials is quantified using slide-hold-slide tests. This restrengthening has implications on the reactivation potential of granular shear zones in analogue models. With the provided software we first analyze the experimental data and then compare the angles and stresses needed to reactivate normal faults in the materials. We find that while healing rates are low, the majority of samples can not reactivate normal faults that are generated through extension of an analogue model.
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