ALBERT

Description: 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

Description: The source parameter, stress drop, is a key parameter estimating strong ground motion, as it controls the level of peak ground acceleration. Several studies have investigated source parameters for many regions by various methods and assumptions (e.g., Japan, Southern California, and central Italy). In this study, we apply a non-parametric spectral decomposition to isolate the source, site, and path contributions on the Fourier amplitude spectra (FAS) in Europe. We propose the two-domain regionalized attenuation models in the spectral decomposition to take into account the varying attenuation of two spatial domains along the Alps. We download all available data from 1990 to 2020 in Europe through the European Integrated Data Archive (EIDA) within the stream2segment software. The analyzed FAS are selected from 35.6 million recordings which contain 6,135 earthquakes recorded by about 1,600 stations. The final data includes the induced events in Groningen and Poland and some significant events in Italy which provide the opportunity to discuss the variation between induced and natural earthquakes. To derive comparable source parameters in such a large study area, we set one station, LLS (Linth-Limmern), in the Swiss network as a reference site to obtain the corrected source spectra. Finally, we fit the source spectra with a standard ω2-model. The resulting stress drop shows a positive correlation with earthquake magnitude in the magnitude range of 2 to 4.5 and a constant value for larger magnitudes (M〉4.5).

Description: 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.

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: High-precision real-time GNSS have recently expanded to monitoring and early detection of natural hazards. German research project EWRICA (Early-Warning and Rapid ImpaCt Assessment with real-time GNSS in the Mediterranean) funded by the national Ministry for Education and Research aims for the prototype implementation of the GNSS-augmented seismic source inversion and rapid impact assessment in the seismically active regions of Mediterranean. The project runs in close cooperation with partners operating high-rate GNSS networks RING (Italy) and NOANET (Greece). An overarching goal is to compute robust local ground motion models shortly after an earthquake to assess areas of strong shaking as well as secondary effects such as tsunamis and landslides. The four work packages - (1) real-time processing of coseismic displacements (RT-multi GNSS with regional augmentation, streamed in miniSEED format via SeedLink server, optionally joint processing with collocated accelerometers); (2) fast source inversion (Bayesian moment-tensor solution with Pyrocko tools); (3) rapid impact assessment (neural network predictions of ground motion maps with uncertainties, also coupled to probabilistic tsunami forecasting PTF at INGV); and (4) system prototype -- end up with an operational system prototype to demonstrate the full operational processing chain by hindcasting selected historical (e.g., 2016 M6.2 Norcia; 2020 M7 Samos) and synthetic event scenarios. EWRICA may serve as a blueprint for other regions of the world: currently EWRICA's tools are being tested for application in Indonesia, together with the colleagues from the Geospatial Agency (BIG) and from the national tsunami warning center InaTEWS.

Description: We investigate the source scaling and ground-motion variability of 1585 earthquakes with Mw 〉 3 occurring along the East Anatolian fault since 2010. We compile a dataset of 17,691 Fourier amplitude spectra of S waves recorded by 186 stations. A spectral decomposition is applied to isolate the source contribution from propagation and site effects. Source spectra are fit with Brune’s model to estimate seismic moment and corner frequency and to compute the stress drop Δσ. The 10th, 50th, and 90th percentiles of the Δσ distribution are 0.18, 0.51, and 1.69 MPa, respectively, and the average Δσ increases with earthquake magnitude. For the two mainshocks of the 2023 sequence, the estimated Δσ is about 13 MPa, significantly larger than the Δσ of the smaller events. At intermediate and high frequencies, the interevent residuals are correlated with Δσ. When recorded peak ground accelerations and velocities for Mw 〈 6 are compared with the predictions from ground-motion models proposed in the literature, the negative value of the average interevent residuals is consistent with low values of Δσ. Contrariwise, the average residuals for the peak parameter of the Mw 7.8 and 7.5 mainshocks of the 2023 sequence are almost zero, but with distance dependencies.

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: This archive disseminated through the GFZ-Data Service includes both results and information as-sociated to Bindi et al. (2023). In particular, the archive includes a seismic catalogue reporting ener-gy magnitude Me estimated form vertical P-waves recorded at teleseismic distances in the range 20°≤ D ≤ 98°, following Di Giacomo et al (2008, 2010). The catalogue is built considering 6349 earth-quakes included in the GEOFON (Quinteros et al, 2021) catalogue with moment magnitude Mw larger than 5 and occurring after 2011. Tools used to compute the energy magnitude are free available. In particular, we used stream2segment (Zaccarelli, 2018) to download data from IRIS (https://ds.iris.edu/ds) and EIDA (Strollo et al., 2021) repositories, and me-compute [Zaccarelli, 2023) to process waveforms and compute Me. The methodology applied to me-compute is also implemented as add-on for SeicomP (GFZ and Gempa, 2020) in order to allow the real time computation of Me (https://github.com/SeisComP/scmert).

Description: We present a seismic catalog (Bindi et al., 2024, https://doi.org/10.5880/GFZ.2.6.2023.010) including energy magnitude Me estimated from P waves recorded at teleseismic distances in the range 20° 1 98° and for depths shorter than 80 km. The catalog is built starting from the event catalog disseminated by GEOFON (GEOFOrschungsNetz), considering 6349 earthquakes with moment magnitude Mw 5 occurring between 2011 and 2023. Magnitudes are computed using 1 031 396 freely available waveforms archived in EIDA (European Integrated Data Archive) and IRIS (Incorporated Research Institutions for Seismology) repositories, retrieved through the standard International Federation of Digital Seismograph Networks (FDSN) web services (https://www.fdsn.org/webservices/, last access: March 2024). A reduced, high-quality catalog for events with Mw 5〉_8 and from which stations and events with only few recordings were removed forms the basis of a detailed analysis of the residuals of individual station measurements, which are decomposed into station- and event-specific terms and a term accounting for remaining variability. The derived Me values are compared to Mw computed by GEOFON and with the Me values calculated by IRIS. Software and tools developed for downloading and processing waveforms for bulk analysis and an add-on for SeisComP for real-time assessment of Me in a monitoring context are also provided alongside the catalog. The SeisComP add-on has been part of the GEOFON routine processing since December 2021 to compute and disseminate Me for major events via the existing services.