ALBERT

Beschreibung: In Bindi et al. (2019) a harmonized local magnitude scale across Europe has been derived using data disseminated by network operators through the European Integrated Data Archive (EIDA). This data set contains regionalized non-parametric attenuation tables, attenuation corrections to the parametric model and station corrections for both non-parametric and parametric models for more than 2000 stations in Europe. Regionalization has been performed considering six different regions covering Europe and the polygons defining them are also provided. Data are subject to updates that can be triggered by the availability of new and substantial input data (reviewed earthquake catalogues and/or new waveforms). Each update will be released with a new version of the data. The data are provided in ASCII format (.csv).

Beschreibung: The task of downloading comprehensive datasets of event-based seismic waveforms has been made easier through the development of standardised web services, but is still highly non-trivial, as the likelihood of temporary network failures or even worse subtle data errors naturally increase when the amount of requested data is in the order of millions of relatively short segments. This is even more challenging as the typical workflow is not restricted to a single massive download but consists of fetching all possible available input data (e.g., with several repeated download executions) for a processing stage producing any desired user-defined output. Here, we present stream2segment, a highly customisable Python 2+3 package helping the user through the whole workflow of downloading, inspecting and processing event-based seismic data by means of a relational database management system as archiving storage, which has clear performance and usability advantages. Stream2segment provides an integrated processing implementation able to produce any kind of user-defined output based on a configuration file and a user-defined Python function. Stream2segment can also produce diagnostic maps or user-defined plots which, unlike existing tools, do not require external software dependencies and are not static images but interactive browser-based applications ideally suited for data inspection or annotation tasks.

Beschreibung: We present a Python application to download events and data from FDSN webservices (https://www.fdsn.org/webservices/) and compute the events energy Magnitude (Me), producing outputs in several formats (QuakeML, HDF, CSV, HTML). This software has been used to compile a seismic catalogue including Me estimated form P-waves recorded at teleseismic distances in the range 20° ≤ ∆ ≤ 98°, available at GFZ Data Services (Bindi et al., 2023; https://doi.org/10.5880/GFZ.2.6.2023.010). The software complete pipeline (download and energy magnitude computation) can be deployed locally via terminal commands or chained and scheduled on a server to compute the energy magnitude in semi-realtime (e.g. daily or weekly).

Beschreibung: The increasingly high number of big data applications in seismology has made quality control tools to filter, discard, or rank data of extreme importance. In this framework, machine learning algorithms, already established in several seismic applications, are good candidates to perform the task flexibility and efficiently. sdaas (seismic data/metadata amplitude anomaly score) is a Python library and command line tool for detecting a wide range of amplitude anomalies on any seismic waveform segment such as recording artifacts (e.g., anomalous noise, peaks, gaps, spikes), sensor problems (e.g., digitizer noise), metadata field errors (e.g., wrong stage gain in StationXML). The underlying machine learning model, based on the isolation forest algorithm, has been trained and tested on a broad variety of seismic waveforms of different length, from local to teleseismic earthquakes to noise recordings from both broadband and accelerometers. For this reason, the software assures a high degree of flexibility and ease of use: from any given input (waveform in miniSEED format and its metadata as StationXML, either given as file path or FDSN URLs), the computed anomaly score is a probability-like numeric value in [0, 1] indicating the degree of belief that the analyzed waveform represents an anomaly (or outlier), where scores ≤0.5 indicate no distinct anomaly. sdaas can be employed for filtering malformed data in a pre-process routine, assign robustness weights, or be used as metadata checker by computing randomly selected segments from a given station/channel: in this case, a persistent sequence of high scores clearly indicates problems in the metadata

Beschreibung: Ground motion models (GMM) have been employed in several domains, from traditional seismic hazard and risk analysis to more recent shakemaps and rapid loss assessment. In this framework, eGSIM is a Python package and web application intended to help engineers and seismologist in understanding how different models compare for specific earthquake scenarios and how well they fit to observed ground motion data, producing results as visual plot or tabular data in standard, accessible and convenient formats (CSV, HDF, JSON and several image formats). Based on OpenQuake, a popular open-source Python library for seismic hazard and risk analysis, eGSIM incorporates and makes available in two user-friendly interfaces hundreds of published GMMs implemented and tested in OpenQuake: an online graphical user interface (GUI) accessible at https://egsim.gfz-potsdam.de, ideal for comparisons that can be visualized or downloaded as images, and a web application programming interface (web API), implemented along the lines of popular seismological web services (FDSN), more suited for comparisons that may be automatized in scheduled jobs, or need to be integrated into custom code and further processed in the user's own workflows. By incorporating databases in form of so-called flatfiles (ESM) and regionalizations derived from seismic hazard models (SHARE, ESHM20), eGSIM allows users to seamlessly select data for comparison and models for comparison based on regions of interest. It also features management scripts to smoothly incorporate new flatfiles or regionalizations from future research projects.Moreover, via the generation of flatfile templates based on a custom selection of GMMs, and the possibility to upload user-defined flatfiles, eGSIM facilitates the non-trivial task of compiling data for model comparison, and can be used to analyze ground motions from any data set recorded anywhere in the world, including rapid analysis of earthquake records following large events.

Beschreibung: 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.

Beschreibung: 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.