Grond is an open source software tool for robust characterization of earthquake sources. Moment tensors and finite fault rupture models can be estimated from a combination of seismic waveforms, waveform attributes and geodetic observations like InSAR and GNSS. It helps you to investigate diverse magmatic, tectonic, and other geophysical processes at all scales.
It delivers meaningful model uncertainties through a Bayesian bootstrap-based probabilistic joint inversion scheme. The optimisation explores the full model space and maps model parameter trade-offs with a flexible design of objective functions.
Rapid forward modelling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. They serve synthetic near-field surface displacements and synthetic seismic waveforms for arbitrary earthquake source models and geometries.
The processing of Persistent Scatterer Interferometry (PSI) data and the estimation of displacement is a nonlinear and user-driven procedure that can introduce large errors for noisy backscatter points. Results may differ significantly depending on chosen thresholds, filter settings, constraints and final interpretation. Thus the identification of valid PS with rather low errors in the SAR data is a crucial step in the PSI workflow. PSI-Explorer is a scientific prototype of our visual-analytics (VA) approach supporting this important task. The prototype is written in Java and operates on Matlab files.
Contributors:Porwollik, Vera, Rolinski, Susanne, Müller, Christoph
Tillage is a central element in agricultural soil management and has direct and indirect effects on processes in the biosphere. Effects of agricultural soil management can be assessed by soil, crop, and ecosystem models but global assessments are hampered by lack of information on soil management systems. This study presents a classification of globally relevant tillage practices and a global spatially explicit data set on the distribution of tillage practices for around the year 2005. This source code complements the dataset on the global gridded tillage system mapping described in Porwollik et al. (2018, http://doi.org/10.5880/PIK.2018.012). It shall help interested people in understanding the findings on the global gridded tillage system mapping. The code, programmed in R, can be used for reproducing and build upon for scenarios including the expansion of sustainable soil management practices as CA. Both, the data set and the R-code are described in detail in Porwollik et al. (2018, ESSD). The code is written in the statistical software 'R' using the 'raster', 'fields', and 'ncdf4' packages. We present the mapping result of six tillage systems for 42 crop types and potentially suitable Conservation Agriculture area as variables:1 = conventional annual tillage2 = traditional annual tillage3 = reduced tillage4 = Conservation Agriculture5 = rotational tillage6 = traditional rotational tillage7 = potential suitable Conservation Agriculture area Reference system: WGS84Geographic extent: Longitude (min, max) (-180, 180), Latitude (min, max) (-56, 84)Resolution: 5 arc-minutesTime period covered: around the year 2005Type: NetCDF Dataset sources (with indication of reference):1. Grid cell allocation key to country: IFPRI/IIASA (2017, cell5m_allockey_xy.dbf.zip)2. Crop-specific physical cropland: IFPRI/IIASA (2017, spam2005v3r1_global_phys_area.geotiff.zip)3. SoilGrids depth to bedrock: Hengl et al. (2014)4. Aridity index: FAO (2015)5. Conservation Agriculture area: FAO (2016)6. Income level: World Bank (2017)7. Field size: Fritz et al. (2015)8. Water erosion: Nachtergaele et al. (2011)
GeoMultiSens developed an integrated processing pipeline to support the analysis of homogenized data from various remote sensing archives. The processing pipeline has five main components: (1) visual assessment of remote sensing Earth observations, (2) homogenization of selected Earth observation, (3) efficient data management with XtreemFS, (4) Python-based parallel processing and analysis algorithms implemented in a Flink cloud environment, and (5) visual exploration of the results. GeoMultiSens currently supports the classification of land-cover for Europe.
Geoarchives are an important source to understand the interplay of climate and landscape developments in the past. One important example are sediment cores from the ground of lakes. The microfacies-explorer is a Java-based prototype, that provides a tailored combination of visual and data mining methods enabling scientists to explore categorical data from geoarchives.
The validation of a simulation model is a crucial task in model development. It involves the comparison of simulation data to observation data and the identification of suitable model parameters. SLIVISU is a Visual Analytics framework that enables geoscientists to perform these tasks for observation data that is sparse and uncertain. Primarily, SLIVISU was designed to evaluate sea level indicators, which are geological or archaeological samples supporting the reconstruction of former sea level over the last ten thousands of years and are compiled in a postgreSQL database system. At the same time, the software aims at supporting the validation of numerical sea-level reconstructions against this data by means of visual analytics.
The file is an XML Graph file, which can be used to process Sentinel-1 satellite images in the Sentinel Application Platform (SNAP). Using this file enables batch processing of Sentinel-1 (IW, GRDH) images. The preprocessing is optimized for land use classification.
The following tools are executed:
Apply Orbit File
Speckle Filter (Gamma Map 3x3)
Range-Doppler Terrain Correction
Pyrocko is an open source seismology toolbox and library, written in the Python programming language. It can be utilized flexibly for a variety of geophysical tasks, like seismological data processing and analysis, calculation of Green's functions and earthquake models' synthetic waveforms and static displacements (InSAR or GPS). Those can be used to characterize extended earthquake ruptures, point sources (moment tensors) and other seismic sources. This publication includes the Pyrocko core, a library providing building blocks for researchers and students wishing to develop their own applications.
The Pyrocko framework also ships with application: (1) Snuffler (interactive seismogram browser and workbench), (2) Cake (1D travel-time and ray-path computations), (3) Fomosto (calculate and manage Green’s function databases) and (4) Jackseis (waveform archive data manipulation).
Additional applications, as of Grond, Lassie and Kite are individual software publications. See the project page (www.pyrocko.org) for full documentation, tutorials and installation instructions.
This code is a python implementation of the p- and s-wave velocity to density conversion approach after Goes et al. (2000). The implementation has been optimised for regular 3D grids using lookup tables instead of Newton iterations.
Goes et al. (2000) regard the expansion coefficient as temperature dependent using the relation by Saxena and Shen (1992). In `Conversion.py`, the user can additionally choose between a constant expansion coefficient or a pressure- and temperature dependent coefficient that was derived from Hacker and Abers (2004).For detailed information on the physics behind the approach have a look at the original paper by Goes et al. (2000).
Up-to-date contact information are given on the author's github profile https://github.com/cmeessen.
This service provides routing information for distributed data centres, in the case where multiple different seismic data centres offer access to data and products using compatible types of services. Examples of the data and product objects are seismic timeseries waveforms, station inventory, or quality parameters from the waveforms. The European Integrated Data Archive (EIDA) is an example of a set of distributed data centres (the EIDA „nodes“). EIDA have offered Arclink and Seedlink services for many years, and now offers FDSN web services, for accessing their holdings. In keeping with the distributed nature of EIDA, these services could run at different nodes or elsewhere; even on computers from normal users. Depending on the type of service, these may only provide information about a reduced subset of all the available waveforms.
To be effective, the Routing Service must know the locations of all services integrated into a system and serve this information in order to help the development of smart clients and/or services at a higher level, which can offer the user an integrated view of the entire system (EIDA), hiding the complexity of its internal structure.
The service is intended to be open and able to be queried by anyone without the need of credentials or authentication.