ALBERT

Description: Geochemical models are used to seek answers about composition and evolution of groundwater, spill remediation, viability of geothermal resources and other important geoscientific applications. To understand these processes, it is useful to evaluate geochemical model response to different input parameter combinations. Running the model with varying input parameters creates a large amount of output data. It is a challenge to screen this data from the model to identify the significant relationships between input parameters and output variables. For addressing this problem we developed a Visual Analytics approach in an ongoing collaboration between Geoinformatics and Hydrogeology sections of GFZ German Research Centre for Geosciences. We implement our approach as an interactive data exploration tool called the GCex. GCex is a Visual Analytics approach and prototype that supports interactive exploration of geochemical models. It encodes many-to-many input/output relationships by the simple yet effective approach called Stacked Parameter Relation (SPR). GCex assists in the setup of simulations, model runs, data collection and result exploration, greatly enhancing the user experience in tasks such uncertainty and sensitivity analysis, inverse modeling and risk assessment. While in principle model-agnostic, the prototype currently supports and is tied to the popular geochemical code PHREEQC. Modification to support other models would not be complicated. GCex prototype was originally written by Janis Jatnieks at GFZ-Potsdam. It relies on Rphree (R-PHREEQC geochemical simulation model interface) written by Marco De Lucia at GFZ-Potsdam. A compatible version of Rphee is bundled with this installation.

Description: GRACE/GRACE-FO Level-3 product based on GFZ RL06 Level-2B products (Dahle & Murböck, 2019) representing Terrestrial Water Storage (TWS) anomalies provided at 1° latitude-longitude grids as defined over all continental regions except Greenland and Antarctica. The TWS anomaly grids are provided in NetCDF format divided into yearly batches. The files each contain four different variables: 1) 'tws': gravity-based TWS 2) 'std_tws': gravity-based TWS uncertainties 3) 'leakage': spatial leakage contained in TWS 4) 'model_atmosphere': background model atmospheric mass These Level-3 products are visualized at GFZ's web portal GravIS (http://gravis.gfz-potsdam.de). Link to data products: ftp://isdcftp.gfz-potsdam.de/grace/GravIS/GFZ/Level-3/TWS

Description: Operations such as time and coordinate conversions and data cleaning are routine tasks in geodesy and geophysics. Nevertheless, simple and efficient high-level functions to help those kinds of jobs are barely available, and has to be developed, again and again, by each student, engineer for each new project, and even by senior scientists. On another hand, Python became little by little within the last decade a well-used programming language in the academic world. Despite the fact that countless toolboxes already exist in Python for scientific purposes, none really exists for geodetic-oriented purposes. The geodeZYX toolbox aims to fill this gap. The objective of this toolbox, written in Python 3, is to provide a simple but useful and efficient set of functions to help geodesists and geophysicists to spend less time on the pre-processing steps and focus faster on their research, according to the KISS Principle.

Description: SMARTIE1 is a joint seismological experiment of the Karlsruhe Institute of Technology (KIT) and the Leipzig University. We installed in total 36 seismic stations as ring-like and profile-like measurements near to a single wind turbine (WT) at the Fraunhofer Institute for Chemical Technology (ICT) in Pfinztal, SW Germany, for 21 days. The main goals of this project are a better understanding of a single WT as a seismic source and the development of propagation models for the WT-induced seismic signals, depending on the geological properties. Waveform data are available from the GEOFON data centre, under network code X8, and are embargoed until end of 2019.

Description: The data available in this supplementary publication are: - A folder (2019-003_Corbi-et-al_Fig6.zip) containing: 1. top-view pictures (e.g. ‘lunayyr1_0025.JPG’) and displacement data obtained with MatPiv (e.g. ‘uun25.mat’ and ‘uvn25.mat’; dike parallel and orthogonal components; respectively) shown in figure 6 of Xu et al 2016. 2. a Matlab script (‘fig6_a_h.m’) that allows reproducing the same figure setup as in figure 6 panels a-h of Xu et al 2016. The thick red line highlights dike position. The background shading refers to dike orthogonal displacement. - A folder (2019-003_Corbi-et-al_PIV_data.zip) containing: 1. surface deformation data obtained with MatPiv. Each file (‘vel_fine_piv#.mat’) contains 4 elements (x, y, u, v) representing the coordinates and horizontal and vertical component of incremental velocity field organized in a 143 x 215 matrix; 2. the run_movie.m Matlab script. Running it the user can visualize the space-time evolution of cumulative surface displacement. The background shading refers to dike orthogonal component of displacement. The thick red line highlights dike position. - A folder (2019-003_Corbi-et-al_pictures.zip) containing the whole set of pictures from the experiment shown in Xu et al., 2016. - A movie (2019-003_Corbi-et-al_graben formation.mp4) obtained using the whole set of pictures (96 photos). The thick red line highlights dike position. The amount of dike opening is reported as header. - A movie (2019-003_Corbi-et-al_cum_displacement.mp4) showing the space-time evolution of cumulative surface displacement, where the background shading refers to dike orthogonal component of displacement. The thick red line highlights dike position.

Description: BEAT is an open-source software tool for the robust characterization of the temporal and spatial evolution of earthquake rupture processes. It uses kinematic rupture models that include low-parametric models like Moment Tensors but also complex high-parametric, finite-extent sources. In other words, BEAT allows studying earthquakes on a first-order level as points with location, size and mechanisms. In consecutive steps, the complexity of the source model may be increased by various details up to the potential to resolve rupture dimension, fault segmentation, slip-distribution and slip-history. The source model parameters and their uncertainties are estimated based on seismic waveforms, and/or geodetic observations like InSAR and GNSS data. Rapid forward modeling is enabled by using pre-computed Green's function databases, handled through the Pyrocko software library. Based on these, synthetic data are provided for arbitrary earthquake rupture models embedded in heterogeneous media. For an extensive exploration of the often high-dimensional model parameter space, BEAT offers a suite of sampling algorithms for high-standard Bayesian inference. The implementations of these sampling algorithms exploit the parallel architecture of modern computers for optimal performance. Finally, BEAT offers easy configuration and automatic visualization of relevant results. The software relies on functionality from PYROCKO (Heimann et al., 2017) and KITE (optionally, Isken et al., 2017).

Description: This data set includes 40 videos (+ 1 image) depicting the surface evolution of 39 experiments on crustal extension, as well as 4D CT imagery (figures and videos) of 6 of these experiments. The experiments examined the influence of the method for driving extension (foam base, rubber base, plate base or conveyor base) for localization of deformation in overlying layers of brittle-only and brittle-viscous materials representing the earth’s crust. All experiments were performed at the Tectonic Modelling Laboratory of the University of Bern. Detailed descriptions of the experiments and monitoring techniques can be found in Zwaan et al. (2019) to which these data are supplementary material. All experiments were monitored with top view photographs (SLR camera Nikon D-100 6.1 MPx). The photograph time steps depend on the applied extension velocity, but are generally 1 or 2 min. Six experiments were also monitored with an X-Ray computed tomography technique using a 64 slice Siemens Somatom Definition AS X-ray CT-scanner (Zwaan et al., 2016) with varying time intervals (5-30 min). CT-data was analyzed with the software OsiriX (Pixmeo SARL).

Description: Extensive passive seismic monitoring was carried out between September 2017 and September 2018 over the Los Humeros geothermal field in Mexico. This experiment, in addition to several geophysical, geological, and geochemical surveys was conducted in the framework of the European H2020 and Mexican CONACyT-SENER project GEMex for a better understanding of the structures and behavior of the local geothermal system currently under exploitation, and for investigating future development areas. 25 broadband stations (22 Trillium C-120s and 3 Trillium C-20 PH) recording at 200 Hz, and 20 short period stations (Mark L-4C-3D) recording at 100 Hz comprised the network which is sub-divided into two sub-networks. An inner and denser (~1.6-2 km inter-station distance) pseudo-rhomboidal array (27 stations) was laid out to cover the producing zone and retrieve local seismicity mainly associated to injection and production operations, and to comply with beamforming of ambient noise and time reverse imaging techniques. An outer and sparser (~5 km minimum spacing) array was placed at around 30 km radius surrounding the inner network, and was mainly dedicated to larger scale imaging techniques, such as seismic ambient noise tomography, and regional earthquakes tomography. The GEMex project is supported by the European Union’s Horizon 2020 programme for Research and Innovation under grant agreement No 727550 and the Mexican Energy Sustainability Fund CONACYT-SENER, project 2015-04-68074. Waveform data are available from the GEOFON data centre, under network code 6G, and are embargoed until January 2023.

Description: The Liquiñe-Ofqui fault system (LOFS) in south-central Chile provides a natural laboratory to assess the interplay between magma/hydrothermal fluid flow and crustal deformation. Understanding these processes is of paramount importance for geothermal energy exploration and seismic hazard assessment. We deployed a dense seismic network (Sielfeld et al., 2019) at the northern termination of the LOFS in south-central Chile (~38°S) between 2014 March and 2015 June. The main aim was to better understand the significance and implications of seismic activity in relation to geological information such as the complex fault-fracture network, volcanoes, and the stress field estimated from geological data. As a result, the network was designed to monitor the northern segment of the LOFS on a more regional scale rather than concentration on the activity of one volcano. The network covered a ~200‐km‐long section of the Southern Volcanic Zone, including several Holocene stratovolcanoes (Callaqui, Copahue, Caviahue Caldera, Tolhuaca, Lonquimay, Llaima, Sierra Nevada, Sollipulli, Villarrica, Quetrupillán, Lanín (La), and Mocho‐Choshuenco). Waveform data are open and available under network code 3H from the GEOFON data centre under license CC BY 4.0.

Description: The unrest of el Hierro Islands started in 2011 with a submarine volcanic eruption. In order to better characterize unrest of El Hierro Island 9 landstations were installed on the Island of ElHierro (Figure 1) between March 2015 and June 2016. General remarks about the seismological data: Data quality is very good due to the low anthropogenic noise level of the Island of ElHierro. All stations register with 200 samples per second. H09S has timing problems, most likely related fast-growing scrub and bushes around the station. The broadband stations H01B and H07B only registered the first 30 minutes of every hour. Recorders were EarthData PR 6-24 3-channel Short period seismometers were MARK L-4C-3D Broadband sensors were: Guralp-CMG-3ESP 3-channel seismometer Waveform data is available from the GEOFON data centre, under network code 2L, and is embargoed until January 2021.

Description: Definitive digital values of the Earth's mangetic field recorded during 2014 at INTERMAGNET observatories around the world. Data includes minute, hourly and daily vector values, along with observatory baseline values for quality control. Annual means are also included. All data is included on the single downloadable archive file (gzipped tar format) available from this landing page. This is the 24th annual publication in the series. Some national data institutions may have related DOIs that describe subsets of the data. These DOIs are shown under "Related DOIs to be quoted". For more information on the data formats used in this publication and the technical standards used to create the data, please refer to the INTERMAGNET Technical Manual and the Technical note TN6 "INTERMAGNET Definitive One-second Data Standard".

Description: A line of 6 broadband seismometers have been deployed across a ridge in the Hualien County (Eastern Taiwan). From March 2015 to June 2016 the network has been continuously recording waves incoming from the Taiwanese regional seismicity. During that period, more than 2000 earthquakes with magnitudes Ml〉3 and distant from less than 200km were recorded. The hill is well approximated by a triangular topography of 3600m in length by 900m in height. Waveform data are open and available from the GEOFON data centre, under network code 5K.

Description: Multi-resolution exposure model for seismic risk assessment in the Kyrgyz Republic. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of 1'175 geo-cells covering the territory of the Kyrgyz Republic. The model integrates around 6'000 building observations (see related dataset Pittore et al. 2019). The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: Multi-resolution exposure model for seismic risk assessment in Kazakhstan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Kazakhstan (provided as a separate file). The model prior is based on user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: Multi-resolution exposure model for seismic risk assessment in Uzbekistan. The model has been developed according to the methodology outlined in Pittore, Haas and Silva (2020) "Variable resolution probabilistic modeling of residential exposure and vulnerability for risk applications", Earthquake Spectra. The model is aggregated over a Central Voronoidal Tessellation (CVT) composed of geo-cells covering the territory of Uzbekistan (provided as a separate file). The model prior is based on empirical observations in Kyrgyzstan and Tajikistan as well as user-elicited knowledge. The following specific modelling parameters have been employed: Two exposure models are provided, with prior strength pw 10 and 100. Both models have epsilon=0.001 (see publication indicated in the metadata for details on the modelling process). For each geo-cell the model includes the expected number of buildings , total occupancy and replacement cost for each of the 15 building types defined in the EMCA taxonomy (see Pittore et al, 2019b), plus the buildings that are belonging to other, non specified typologies (described by building type OTH). Each geo-cell also includes the area of the geo-cell itself in squared km. The data package contains three components: 1) exposure models in .csv 2) exposure models in .xml - the file is encoded in NRML 0.5 format and is compatible with the GEM openquake processing engine 3) shapefile of the tessellation that aggregates the exposure model. The field "cell_id" is the linkage with the exposure models

Description: This data publication contains the data sets of a study aiming to reconstruct environmental conditions during the Holocene in the upper part of the Kali Gandaki valley, Nepal. The data are for samples taken from paleosol sections in the Upper Mustang region (Menges et al. 2019). On these samples we measured the grain size distribution to gain information about the depositional processes, pollen data to reconstruct past vegetation, 14C isotopes in the humin fraction of organic matter for soil formation ages, and hydrogen isotopic composition on n-alkanes to reconstruct past hydrological conditions. This is complemented with optically stimulated luminescence data for additional depositional age information, surface water samples and modern soil samples to constrain modern hydrological conditions, and sediment concentration data to gain insights into erosion processes. The data was generated between 2013-02 and 2018-12. The data files are provided in Excel and tab-delimited text versions.

Description: In geosciences the discretization of complex 3D model volumes into finite elements can be a time-consuming task and often needs experience with a professional software. Es-pecially outcropping or out-pinching geological units, i.e. geological layers that are rep-resented in the model volume, pose serious challenges. Changes in the geometry of a model may occur well into a project at a point, when re-meshing is not an option any-more or would involve a significant amount of additional time to invest. In order to speed up and automate the process of discretization, Apple PY (Automatic Portioning Preventing Lengthy manual Element assignment for PYthon) separates the process of mesh-generation and unit assignment. It requires an existing uniform mesh together with separate information on the depths of the interfaces between geological units (herein called horizons). These two pieces of information are combined and used to assign the individual elements to different units. The uniform mesh is created with a standard meshing software and contains no or only very few and simple structures. The mesh has to be available as an Abaqus input file. The information on the horizons depths and lateral variations in the depths is provided in a text file. Apple PY compares the ele-ment location and depth with that of the horizons in order to assign each element to a corresponding geological unit below or above a certain horizon.

Description: This catalogue is the extended version of “The European-Mediterranean Earthquake Catalogue (EMEC) for the last millennium” (Grünthal and Wahlstrom, 2012, 2012). It is an earthquake catalogue for tectonic events in the area of European Mediterranean, including the Mid-Atlantic ridge down to the Azores, extends in the south to Africa north of the Sahara, in the north to the Arctic Sea, and in the east to the Levant, eastern Turkey, and the Caucasus. This areal coverage gave the name to the catalogue: EMEC—The European-Mediterranea Earthquake Catalogue. It extends the previous version by the years 2007 to 2021 and thus contains tectonic events for the period AD 1000 to 2021 with a uniform magnitude Mw from the threshold of 3.5. The dataset contains 61140 entries.

Description: Preparation of technical reports can be unwieldy. However, a significant proportion of the document structure is often standardised. The GFZ Report Generator is a Python 2.7 application meant to ease this process by (i) automatically generating the standardised figures and tables, (ii) creating a report template pre-filled with this standard content, and which meets the GFZ style requirements, and (iii) providing a browser-based GUI with a text editor where users can add content to the report, generate and inspect the HTML and PDF versions on the fly as they are editing, track changes and revert to previous versions, and easily control the document structure and formatting from within the text by typing special characters in reStructuredText, an easy-to-read, what-you-see-is-what-you-get plaintext markup syntax. The GFZ Report Generator is quite flexible and by the use of tailor-made templates can be adapted easily to other use cases, where part of a document is based on standardised figures and section structure. For example, the software is deployed at GEOFON to generate both seismic network reports and annual reports. For the former, GEOFON also offers an online service (https://geofon.gfz-potsdam.de/waveform/reportgenerator/) where PIs and others can easily generate report templates pre-filled with network-specific content (e.g., probability density functions plots) and available online for editing. In this process, the deployed instance of the GFZ Report Generator proved to be useful for finding some classes of problems with the data and metadata stored at GEOFON.

Description: Preparation of technical reports can be unwieldy. However, a significant proportion of the document structure is often standardised. The GFZ Report Generator is a Python 2.7 application meant to ease this process by (i) automatically generating the standardised figures and tables, (ii) creating a report template pre-filled with this standard content, and which meets the GFZ style requirements, and (iii) providing a browser-based GUI with a text editor where users can add content to the report, generate and inspect the HTML and PDF versions on the fly as they are editing, track changes and revert to previous versions, and easily control the document structure and formatting from within the text by typing special characters in reStructuredText, an easy-to-read, what-you-see-is-what-you-get plaintext markup syntax. The GFZ Report Generator is quite flexible and by the use of tailor-made templates can be adapted easily to other use cases, where part of a document is based on standardised figures and section structure. For example, the software is deployed at GEOFON to generate both seismic network reports and annual reports. For the former, GEOFON also offers an online service (https://geofon.gfz-potsdam.de/waveform/reportgenerator/) where PIs and others can easily generate report templates pre-filled with network-specific content (e.g., probability density functions plots) and available online for editing. In this process, the deployed instance of the GFZ Report Generator proved to be useful for finding some classes of problems with the data and metadata stored at GEOFON.