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Teleseismic back-projection imaging has emerged as a powerful tool for understanding the rupture propagation of large earthquakes. However, its application often suffers from artifacts related to the receiver array geometry. We developed a teleseismic back-projection technique that can accommodate data from multiple arrays. Combined processing of P and pP waveforms may further improve the resolution. The method is suitable for defining arrays ad-hoc to achieve a good azimuthal distribution for most earthquakes. We present a catalog of short-period rupture histories (0.5-2.0 Hz) for all earthquakes from 2010 to 2022 with Mw {greater than or equal to} 7.5 and depth less than 200 km (56 events). The method provides automatic estimates of rupture length, directivity, speed, and aspect ratio, a proxy for rupture complexity. We obtained short-period rupture length scaling relations that are in good agreement with previously published relations based on estimates of total slip. Rupture speeds were consistently in the sub-Rayleigh regime for thrust and normal earthquakes, whereas a tenth of strike-slip events propagated at supershear speeds. Many rupture histories exhibited complex behaviors, e.g., rupture on conjugate faults, bilateral propagation, and dynamic triggering by a P wave. For megathrust earthquakes, ruptures encircling asperities were frequently observed, with down-dip, up-dip, and balanced patterns. Although there is a preference for short-period emissions to emanate from central and down-dip parts of the megathrust, emissions up-dip of the main asperity are more frequent than suggested by earlier results. The data are presented as follows (and described in detail in the associated README): SUPPORTING DATA SET S1 (2024-001_Vera-et-al_Supporting-Data-S1.zip) This Data Set (S1) consists of *.bp files containing (1) short-period earthquake rupture patterns, (2) energy radiated maps, and (3) source time functions derived from back-projections (0.5-2.0 Hz). The Data Set S1 includes 56 folders, representing 56 processed earthquakes between 2010 and 2022 with a moment magnitude (Mw) greater than or equal to 7.5 and a depth less than 200 km. These folders are labeled in the format YYYYMMDDhhmm_EVENT_NAME_REGION (UTC) in *.bp format. SUPPORTING DATA SET S2 (2024-001_Vera-et-al_Supporting-Data-S2.csv) This Data Set (S2) comprises a *.csv file containing earthquake source information used in the back-projection and the resulting rupture parameter estimates based on **visually determined** rupture end times. The *.csv file includes rupture parameter estimates for each of the 56 earthquake back-projections presented in Data Set S1. SUPPORTING DATA SET S3 (2024-001_Vera-et-al_Supporting-Data-S3.csv) This Data Set (S3) comprises a *.csv file containing earthquake source information used in the back-projection and the resulting rupture parameter estimates based on **automatic** rupture end times. Note: The main difference from Data Set S2 is that rupture parameter estimates in S3 are derived from **automated** rupture end times, whereas S2 provided estimates relative to **visually determined** rupture end times.
Ground-motion flatfiles are commonly used to develop ground motion models (GMMs) and for systematical analysis of ground motions over a wide range of distances and earthquake magnitudes. A flatfile is organized as a table of properties and various intensity measures of earthquake waveforms, including data processing parameters. Here we present a comprehensive processed ground-motion flatfile containing data from the Kyoshin (K-NET) and Kiban-Kyoshin (KiK-net) networks operated by National Research Institute for Earth Science and Disaster Resilience (NIED) (2019) in Japan (Okada et al., 2004; Aoi et al., 2011). This flatfile contains 914,628 ground motions from 18,018 events recorded by 1,749 stations. Out of these, 434,898 ground-motions are from KiK-net and 479,730 from K-net. The events were recorded between June 1996 and September 2024, covering distances up to 1200 km and magnitudes between 2.5 and 9. The ground motions have been automatically processed, and metadata describing each event and record are provided in the flat file. An overview of the flatfiles and the processing steps to derive the reported ground-motion parameters is provided in this report. Further details and discussion about the flatfile compilation can be found in the corresponding publication: Loviknes, K., von Specht, S., Lilienkamp, H., Händel, A., and Cotton, F. (2025). Harmonized KiK-net and K-NET flatfile for systematic analysis of earthquake ground motions (submitted to Seismica, February 2025).
This dataset accompanies our study on tremor-like episodes that we discovered in the low-frequency seismic signals preceding the 2023 Mw 7.8 Kahramanmaraş earthquake in Türkiye. Between 12 August 2022 and 6 February 2023, eight months before the mainshock, we identified tremor-like episodes recorded at five seismic stations (NAR, KHMR, MGND, GAZ, and GZT) within a 46 km radius of the mainshock epicenter. Using seismic data from the NAR station (the closest to the mainshock) bandpass-filtered between 1.7 and 2.2 Hz, we identified the start and end times of 3741 tremor-like episodes, resulting in a catalog of 7482 markers. This catalog forms the foundation of the statistical analyses presented in Zali et al. (2025). Additionally, we manually picked the first arrival times of 162 selected pulses recorded between 24 and 31 December 2022 from these episodes across the five stations. Our analysis suggests that these tremor-like episodes originate from an anthropogenic source, likely associated with activities of cement plants located on the Narlı Fault, which hosted the earthquake epicenter. This data publication provides the catalog of start and end times for 3741 tremor-like episodes at the NAR station and the first arrival times of 162 selected pulses recorded at the five stations.
Despite the exponential growth of the amount of ground‐motion data, ground‐motion records are not always available for all distances, magnitudes, and site conditions cases. TFCGAN is a Python software package for modeling and simulating ground shaking to tackle this problem. Based on Esfahani et al. 2023, the software can be used as library in custom code or as command line application and can generate ground-shaking records in different domains (Fourier, Time-Frequency, and Time domains) and different formats (currently numpy, ascii, with foreseen implementation of other formats such as ASDF). The enclosed code and model consist of two steps. In the first step, the generative model simulates ground shaking by conditioning on a set of parameters. In the second step, the time-frequency domain is transferred to the time domain based on the phase retrieval algorithm. The model is conditioned on moment magnitude, distance, and shear wave velocity at the near-surface and trained using the KiK-net database. The proposed model is extended by using a hybrid dataset based on the combination of the European strong motion (ESM) database, near-fault ground-shaking records, and synthetic records. We validate our model based on terms of standard deviations for peak ground accelerations and Fourier amplitude spectral values.
The Earthquake Explorer application was developed at GFZ to provide rapid information on recent earthquakes worldwide as well as earlier earthquakes back to August 2007. It combines a zoomable and configurable map overview of activity with a highly customizable filter with more detailed information on dedicated pages for each event. Currently included are: (1) Location and magnitude estimates. First automatic estimates are usually available a few minutes after the origin time, with a subset of events later reviewed manually. (2) Moment tensor solutions (for larger events only). Currently these are all manually reviewed. They improve the understanding of earthquakes because they are a direct snapshot of the deformation of the surrounding rock by the seismicity. (3) Predicted shake maps (predicted ground motion) for each event based on event parameters and an estimate of the tectonic environment. Additional information about recent events will be included in future developments of the Earthquake Explorer platform. The Earthquake Explorer is open-source, and uses the Data Analytics Software Framework (DASF).
The Early-Warning and Rapid Impact Assessment with real-time GNSS in the Mediterranean (EWRICA) is a federal Ministry of Education and Research funded project (funding period: 2020-2023) that aims to develop fast kinematic and point source inversion and modeling tools combining GNSS-based near field data with traditional broadband ground velocity and accelerometer data. Fast and robust estimates of seismic source parameters are essential for reliable hazard estimates, e.g. in the frame of tsunami early warning. Hence, EWRICA aims for the development and testing of new real time seismic source inversion techniques based on local surface displacements. The resulting methods shall be applied for tsunami early warning purposes in the Mediterranean area. In this framework, this repository is a suite of four packages that can be used and combined in different ways and are ewricacore, ewricasiria, ewricagm and ewricawebapp. These four packages can be deployed in a docker container (see instructions below) to demonstrate a possible output of Early-Warning and Rapid Impact Assessment. In the Docker, a probabilistic earthquake source inversion report (ewricasiria) and a Neural network based Shake map (ewricagm) are generated for two past earthquakes whose data (event and waveform) is continuously served by GEOFON servers at regualr intervals to produce and test a real case scenario. The whole workflow is managed by ewricacore, a central unit of work that first fetches the waveform data via the seedlink protocol and event data via event bus or FDSN web service, then collects and cuts waveforms segments according to a custom configuration, and eventually triggers custom processing (ewricasiria and ewricagm in the docker, but any processing can be implemented) whenever configurable conditions are met. The final package, ewricawebapp is a web-based graphical user interface that can be opened in your local browser or deployed on your web server in order to visualize and check all output produced by the docker workflow in form of HTML pges, images and data in various formats (e.g., JSON, log text files). The EWRICA Docker package includes the following tools: ewricacore: Central unit for all Ewrica components and event/data listener ewricagm: Create ground motion maps via pre-trained Neural Network ewricasiria: Ewrica Source Inversion and Rapid Impact Assessment Python package ewricawebapp: Ewrica web portal and GUI demo grond: A probabilistic earthquake source inversion framework (Heimann et al., 2018) stationsxml-archive: Storage repository for synchronizing Station XMLs
The dataset contains a set of structural and non-structural attributes collected using the GFZ RRVS (Remote Rapid Visual Screening) methodology. It is composed by 6249 randomly distributed buildings in the urban area of Chía (Colombia). The survey has been carried out between May and July 2020 using a Remote Rapid Visual Screening system developed by GFZ and employing omnidirectional images from Google StreetView (and footprints from OpenStreetMap (OSM), both with vintages of May 2020. The buildings were inspected by dozens of local students of civil engineering students from the Universidad de La Sabana (Chía, Colombia). Their attribute values in terms of the GEM v.2.0 taxonomy.
This data publication contains seismic waveform data of 507 earthquakes recorded during the St1 Deep Heat project in June and July 2018, where the 6.1 km deep OTN-3 well near Helsinki, Finland, was hydraulically stimulated over 49 days (Kwiatek et al., 2019). The waveforms were recorded on a surrounding seismic monitoring network consisting of 12 stations, deployed at epicentral distances between 0.6 to 8.2 km and at depths between 0.23 to 1.15 km. Each station consists of three-component, 4.5 Hz, Sunfull PSH geophones, sampling at 500 Hz. The 507 earthquakes analysed were chosen from the relocated event catalogue by Leonhardt et al. (2021a). The dataset is supplementary material to the Geophysical Research Letters research article of Holmgren et al. (2022), which applied the Empirical Green’s Function technique to examine microseismic rupture behaviour at the Helsinki site.
This data publication contains seismic catalog developed by the analysis of seismicity recorded during hydraulic stimulation campaign performed in May 2020 in the 5.8-km deep OTN-2 well near Helsinki, Finland as part of the St1 Deep Heat project (Kwiatek et al., 2022). The original seismic data to develop the seismic catalog were acquired with the high-resolution seismic network composed of 22 geophones surrounding the project site. The centerpiece of the network was a 10-level borehole array of Geospace OMNI-2400 geophones (3C/15 Hz) sampled at 2 kHz placed in the OTN-3 well adjacent to the OTN-2 injection well, and located at 1.93 - 2.55 km depth, approx. 3km from injection intervals. Additional 12 stations at distances <10 km from project site formed the satellite network that was equipped with short-period 3C 4.5 Hz Sunfull PSH geophones, completing the seismic network. Near-real-time processing of induced seismicity data started on Jan 26, 2020, i.e. about 3 months prior to the onset of the injection, covering entire period of the stimulation campaign in May 2020. The monitoring stopped end of June 2020, about one month after the stimulation finished. The monitoring campaign resulted in initial industrial seismicity catalog containing 6,243 events that was refined and further extended (cf. Kwiatek et al., 2022). The final catalog associated with this data publication contains 6,318 earthquakes, including 197, 5427 and 694 events recorded before, during, and after stimulation campaign. The core catalog data contains origin time, local magnitude, (re)location and focal mechanism data.
This folder contains the scripts, input and output files required to calculate the inter-scheme conversion matrices for building types and the implicit damage states of their respective fragility models for two selected vulnerability schemes: one for earthquakes and the other for tsunamis. They were used in previous studies to characterize the residential building stock of Lima. The outcomes generated in this data repository are valuable inputs to then calculate the disaggregated and cumulative damage and losses expected for cascading hazard scenarios.
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