9 documents found in 213ms
# 1
Unger, Andrea • Rabe, Daniela • Eggert, Daniel • Dransch, Doris
Abstract: 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.
# 2
Dreiling, Jennifer • Tilmann, Frederik
Abstract: BayHunter is an open source Python tool to perform an McMC transdimensional Bayesian inversion of receiver functions and/ or surface wave dispersion. It is inverting for the velocity-depth structure, the number of layers and noise parameters (noise correlation and amplitude). The forward modeling codes are provided within the package, but are easily replaceable with own codes. It is also possible to add (completely different) data sets. The BayWatch module can be used to live-stream the inversion while it is running: this makes it easy to see how each chain is exploring the parameter space, how the data fits and models change and in which direction the inversion progresses.
# 3
Sips, Mike • Dransch, Doris • Eggert, Daniel • Freytag, Johann-Christoph • Hollstein, Andre • (et. al.)
Abstract: 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.
# 4
Eggert, Daniel • Sips, Mike • Dransch, Doris
Abstract: gms-vis is a web-based implementation of our visual-analytics approach for assessing remote-sensing data. It is implemented based on the GWT framework. Once deployed through a webserver it acts as the user interface for the GeoMultiSens (GMS) platform. Within the interface users can intuitively define spatial, temporal as well as quality constraints, for remote sensing scenes. A heatmap enables the user to assess the spatial distribution of selected scenes, while a time histogram allows the user to assess their temporal distribution. Finally, users can specify a workflow which will be executed by the GeoMultiSens platform. Though gms-vis is part of the GeoMultiSens platform, it is relatively self-contained and can be attached to different analysis frameworks and platforms with reasonable effort.
# 5
Unger, Andrea • Rabe, Daniela • Klemann, Volker • Eggert, Daniel • Dransch, Doris
Abstract: 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.
# 6
Eggert, Daniel • Köthur, Patrick • Dransch, Doris
Abstract: 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.
# 7
Dietze, Michael
Abstract: Environmental seismoloy is a scientific field that studies the seismic signals, emitted by Earth surface processes. This R package eseis provides all relevant functions to read/write seismic data files, prepare, analyse and visualise seismic data, and generate reports of the processing history. eseis contains a growing set of function to handle the complete workflow of environmental seismology, i.e., the scientific field that studies the seismic signals that are emitted by Earth surface processes. The package supports reading the two most common seismic data formats, general functions for preparational and analytical signal processing aswell as specified functions for handling signals generated by Earth surface processes. Finally, graphical plot functions are provided, too. The software package contains 51 functions and two example data sets (eseis-supplementary_material.zip). It makes use of a series of dependency packages described in the DESCRIPTION file of the package.
# 8
Rawald, Tobias • Sips, Mike • Dransch, Doris
Abstract: PyRQA is a tool to conduct recurrence quantification analysis (RQA) and to create recurrence plots in a massively parallel manner using the OpenCL framework. It is designed to process very long time series consisting of hundreds of thousands of data points efficiently.
# 9
Mills, Steven • Williams, Jack
Abstract: This code (nwrap.ijm) can be used to generate an 'unrolled' circumferential image of a tomographic drill-core scan, such as an X-ray Computed Tomography (CT) scan. The resulting image is analogous to those produced by a DMT CoreScan system®. By comparing such images to geographically references borehole televiewer data, it may be used to reorientate drill-core back into geographic space (Williams et al. submitted). This code should be installed and run as a plugin on ImageJ/Fiji. Full instructions are given in the code and in the Appendix A of Williams et al. (submitted). Examples of unrolled CT scans can be found at Williams et al (2017, http://doi.org/10.5880/ICDP.5052.004).
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