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KiK-NET and K-NET flatfile with automatically processed ground motions

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).

Catalog of Start and End Times of Tremor-Like Episodes Near the Epicenter of the 2023 Mw 7.8 Türkiye Earthquake (12 August 2022 – 6 February 2023)

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.

TFCGAN package: Conditional Generative Models in Time-Frequency domain for Ground motion simulation

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.

A decade of short-period earthquake rupture histories from multi-array back-projection

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.

Earthquake Explorer Package

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).

Probabilistic inter-scheme compatibility matrices for buildings. An application using existing vulnerability models for earthquakes and tsunami from synthetic datasets constructed using the AeDEs form through expert-based heuristics

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.

Creation of simplified state-dependent fragility functions through ad-hoc scaling factors to account for previous damage in a multi-hazard risk context. An application to flow-depth-based analytical tsunami fragility functions for the Pacific coast of South America

This data repository contains a brief description of the building classification scheme for physical vulnerability to tsunamis and corresponding fragility functions originally proposed by Medina, 2019. These fragility functions are used as input to construct their associated state-dependent fragility functions using scaling factors, which were obtained as ad-hoc calibration parameters. A Python script to produce a file with such a model is provided along with the needed inputs and resulting output files.

Tsunami flow depth in Lima/Callao (Peru) caused by six hypothetical simplified tsunami scenarios offshore Lima

This data collection contains six inundation maps in Lima and Callao (Peru) based on tsunami simulations with the wave propagation and run-up model TsunAWI (see Rakowsky et al. 2015). The simulations were carried out in the framework of the RIESGOS project (see riesgos.de). The sources are hypothetical earthquake events in the magnitude range Mw 8.5 to Mw 9.0 offshore Lima. The source area of the events is based on the historical event from October 1746, the parameters are derived from the study Jimenez et al. (2013). The sources are considerably simplified since we aim at a systematic investigation of the tsunami impact and restrict the parameter variation between scenarios to one parameter only, the slip value. The source area is split into five subfaults, however we use a constant slip distribution. The corresponding tsunami simulations are carried out in a triangular mesh with resolution ranging from 7km in the deep ocean to a finest value of about 7m in the coastal land part of the pilot area Lima/Callao. The flow depth distribution in Lima/Callao obtained from the simulation is interpolated to a raster file and provided as Golden Software Binary Grids. The numerical results are obtained from simulations with the finite element model TsunAWI (Rakowsky et al. 2015). The mesh resolution in the pilot area Lima/Callao is approximately 20m, the smallest edge length is about 7m. The main model parameters are listed in Table 1. Concerning the bottom roughness, we use a constant Manning coefficient of 0.02 in all of the model domain.

A Questionnaire Survey of the Earthquake Network App's Users in Peru Following an M8 Earthquake in 2019.

The 'Earthquake Network’ (EQN) is an app which detects earthquakes by creating an ad-hoc network of smartphone's accelerometer sensors and provides early warnings of earthquakes via the same smartphone app. The EMSC (Euro-Mediterranean Seismological Centre) and the University of Bergamo conducted an online survey, following an earthquake of magnitude M8 on 2019-05-26 07:41:13.6 UTC in Northern Peru with epicentre (5.81S, 75.27W). This survey targeted EQN users in the felt area of the earthquakes and was conducted from 2019-07-23 to 2019-08-18. It aimed at assessing users’ understanding and reaction to the EQN early warning for this specific earthquake. The questionnaire was written in Spanish since it is the most commonly spoken language in the studied area. Individuals who use the app in Spanish were invited to complete the survey via an advertisement on the Earthquake Network app. A PDF containing the questionnaire and the relationship between the questions is included in this archive. 3805 respondents took the survey, including 2 719 that were actually in the area at the time. The analysis Results derived from this dataset will be included as part of a submitted Science article (Bossu et al. '“Shaking in 5 seconds!” A Voluntary Smartphone-based Earthquake Early Warning System', 2021) to show that respondents received notifications from the Earthquake Network App before feeling the shaking but also that many did not immediately “drop, hold and cover' and were too intent on warning those close to them of the impending danger. All respondents consented that their data could be used for research purposes. The EMSC and University of Bergamo made sure not to collect or diffuse personal data. The dataset is a zip-file that contains the questionnaire responses as a comma-separated text file (csv) and a pdf containing a representation of the questionnaire that was presented to respondents.

Residential building exposure and physical vulnerability models for ground-shaking and tsunami risk in Lima and Callao (Peru)

This data publication is composed by two main folders: (1) “Top-down_exposure_modelling_Lima” and (2) “Vulnerability_models_Lima/”. The first one contains a complete collection of data models used to represent the residential building portfolio of Lima and Callao (Peru) using a top-down approach (census-based desktop study). Therein, the reader can find a comprehensive description of the procedure of how the exposure models were constructed. This includes python scripts and postprocessed geodatasets to represent these building stock into predefined and separate classes for earthquake and tsunami physical vulnerabilities. The second folder contains sets of fragility functions for these building classes and the assumed economic consequence model. These models are suplement material of a submitted paper (Gomez-Zapata et al., 2021b). Please note it is an unpublished preprint version at the time of writing this document. The reader is strongly advised to look for the definitive version once (if so) it is accepted and published.

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