Flatfiles for the detection and classification of pulse-like ground motions (PULSEFLAT)

Description

PulseFlat is a dataset of near-fault ground motion records characterized by the presence of velocity pulses which are critical features in applied seismology and structural response analysis. The dataset is presented in flatfile format and includes comprehensive waveform metadata, detailed pulse parameters, and classification labels, making it readily usable for data-driven research applications.
The core algorithms for pulse identification and classification are implemented in Python 3 and follow a robust multi-method framework. This framework builds on traditional wavelet-based techniques, incorporating established methodologies from Baker (2007), Shahi and Baker (2014), and Ertuncay and Costa (2019), ensuring consistency and reliability in pulse detection.
PulseFlat extends the near-source flatfiles developed within the NESS2 (Sgobba et al., 2021) and BB-SPEEDset (Paolucci et al., 2021) projects, which relate to both empirical and 3D physics-based simulated ground motion records, respectively. These original datasets have been enriched with newly derived pulse-related parameters and updated classification labels.
PulseFlat is designed to facilitate advanced studies in ground motion modeling and seismic hazard assessment, structural analysis under pulse-like excitation, and the development of machine learning-based approaches.

Figure: Velocity waveform with the phase-shifted Ricker wavelet in red (top panel) used to fit the main pulse of the signal. Power spectrum of the Ricker wavelet (middle panel). Polar plot showing the pulse indicator (PI) values, as defined by Baker (2007), in violet, together with the peak ground velocity (PGV) and the orientations of both the maximum PI and PGV (bottom panel).

Products

  1. PulseFlat on real recorded near-source dataset NESS2.0, distributed as a .zip file, contains:
    • a parametric table (NESS2_flat-file_pulses.csv) in CSV format (semicolon separated) containing elastic spectral acceleration ordinates (5% damped) and other Intensity Measures of ground motion associated to NESS2 flatfile (Sgobba et al., 2021), along with additional pulse metadata;
    • a user manual (User_Manual.pdf) with the explanation of the table fields;
    • supporting dictionaries for some fields of the table.
  2. PulseFlat on simulated waveforms from BB-SPEEDset, distributed as a .zip file, contains:
    • a parametric table (BB_SPEEDset_v1.0_flatfile_pulses.csv) in CSV format (semicolon separated) containing elastic spectral acceleration ordinates (5% damped) and other Intensity Measures of ground motion associated to BB-SPEEDset flatfile (Paolucci et al., 2021), along with additional pulse metadata;
    • a user manual (User_Manual.xlsx) with the explanation of the table fields;
    • a README.txt file with the reference of the original BB-SPEEDset flatfile and the extraction adopted for PULSEFLAT.
    3. Citation
    If you use data available in PulseFlat zip files, cite as:
    Mascandola C., Sgobba S. (2025). PULSEFLAT: Flatfiles for the detection and classification of pulse-like ground motions. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/pulseflat

    If you use the NESS2_flat-file.csv flat-file, cite as: Sgobba, S., Pacor, F., Felicetta, C., Lanzano, G., D'Amico, M. C., Russo, E., & Luzi, L. (2021). NEar-Source Strong-motion flatfile (NESS), version 2.0 (Version 2.0) [Data set]. Istituto Nazionale di Geofisica e Vulcanologia (INGV). https://doi.org/10.13127/NESS.2.0

    If you use the BB-SPEEDset flatfile, cite as: Paolucci, R., Smerzini, C., Vanini, M. (2021). BB-SPEEDset: a validated dataset of broadband near-source earthquake ground motions from 3D physics-based numerical simulations, Bulletin of Seismological Society of America.

    References
    • Sgobba S., Felicetta C., Lanzano G., Ramadan F., D’Amico M., Pacor F. (2021); NESS2.0: An Updated Version of the Worldwide Dataset for Calibrating and Adjusting Ground‐Motion Models in Near Source. Bulletin of the Seismological Society of America; https://doi.org/10.1785/0120210080
    • Paolucci, R., Smerzini, C., Vanini, M. (2021). BB-SPEEDset: a validated dataset of broadband near-source earthquake ground motions from 3D physics-based numerical simulations, Bulletin of Seismological Society of America
    • Baker, J. W. (2007). Quantitative classification of near-fault ground motions using wavelet analysis, Bull. Seismol. Soc. Am. 97, no. 5, 14861501
    • Shahi, S. and Baker J.W. (2014). An Efficient Algorithm to Identify Strong‐Velocity Pulses in Multicomponent Ground Motions. Bulletin of the Seismological Society of America; 104 (5): 24562466; https://doi.org/10.1785/0120130191
    • Ertuncay D., and Costa G. (2019). An alternative pulse classification algorithm based on multiple wavelet analysis. J. Seismol. 2019, 23, 929942

Acknowledgments

This study has been partially developed within the research programs ReLUIS 2024-2026 between INGV and the Italian Dipartimento della Protezione Civile (DPC).

License

Creative Commons License The Pulse Flat Files are licensed under the terms of the "Creative Commons Attribution 4.0 International (CC BY 4.0)" License. This means that you are free to share (reproduce, distribute, communicate to the public, publicly display, perform and play this material in any medium and format) and adapt (remix and build upon the material for any purpose, even commercially). The licensor cannot revoke these freedoms as long as you follow the license terms. You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.

Disclaimer

This site provides access to parametric tables containing near-source strong-motion parameters and associated earthquake, station and waveform metadata. Although all the parameters have been checked by analysts, no warranty, implicit or explicit is attached to the data. Every risk due to the improper use of data or the use of inaccurate information is assumed by the user.