Articles | Volume 25, issue 3
https://doi.org/10.5194/nhess-25-1169-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/nhess-25-1169-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
24 Mar 2025
Research article |
| 24 Mar 2025
| 24 Mar 2025
Tsunami detection methods for ocean-bottom pressure gauges
Department of Physics and Astronomy “Augusto Righi” (DIFA), Alma Mater Studiorum – University of Bologna, Bologna, Italy
Alberto Armigliato
Department of Physics and Astronomy “Augusto Righi” (DIFA), Alma Mater Studiorum – University of Bologna, Bologna, Italy
Martina Zanetti
Department of Physics and Astronomy “Augusto Righi” (DIFA), Alma Mater Studiorum – University of Bologna, Bologna, Italy
Filippo Zaniboni
Department of Physics and Astronomy “Augusto Righi” (DIFA), Alma Mater Studiorum – University of Bologna, Bologna, Italy
Fabrizio Romano
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy
Hafize Başak Bayraktar
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy
Stefano Lorito
Istituto Nazionale di Geofisica e Vulcanologia (INGV), Rome, Italy
Related authors
Filippo Zaniboni, Luigi Sabino, Cesare Angeli, Martina Zanetti, and Alberto Armigliato
EGUsphere, https://doi.org/10.5194/egusphere-2025-1400, https://doi.org/10.5194/egusphere-2025-1400, 2025
Short summary
Short summary
Campi Flegrei volcano is receiving particular attention due to its intense activity and population potentially involved. One of the least considered hazards is the generation of tsunamis from landslides. These are here investigated through numerical simulations, assessing the impact on the Gulf of Pozzuoli coasts. Though more massive, underwater scenarios are found not to pose relevant threats, contrary to subaerial collapses, which can induce significant waves, especially in the near field.
Filippo Zaniboni, Luigi Sabino, Cesare Angeli, Martina Zanetti, and Alberto Armigliato
EGUsphere, https://doi.org/10.5194/egusphere-2025-1400, https://doi.org/10.5194/egusphere-2025-1400, 2025
Short summary
Short summary
Campi Flegrei volcano is receiving particular attention due to its intense activity and population potentially involved. One of the least considered hazards is the generation of tsunamis from landslides. These are here investigated through numerical simulations, assessing the impact on the Gulf of Pozzuoli coasts. Though more massive, underwater scenarios are found not to pose relevant threats, contrary to subaerial collapses, which can induce significant waves, especially in the near field.
Alice Abbate, José M. González Vida, Manuel J. Castro Díaz, Fabrizio Romano, Hafize Başak Bayraktar, Andrey Babeyko, and Stefano Lorito
Nat. Hazards Earth Syst. Sci., 24, 2773–2791, https://doi.org/10.5194/nhess-24-2773-2024, https://doi.org/10.5194/nhess-24-2773-2024, 2024
Short summary
Short summary
Modelling tsunami generation due to a rapid submarine earthquake is a complex problem. Under a variety of realistic conditions in a subduction zone, we propose and test an efficient solution to this problem: a tool that can compute the generation of any potential tsunami in any ocean in the world. In the future, we will explore solutions that would also allow us to model tsunami generation by slower (time-dependent) seafloor displacement.
Enrico Baglione, Stefano Lorito, Alessio Piatanesi, Fabrizio Romano, Roberto Basili, Beatriz Brizuela, Roberto Tonini, Manuela Volpe, Hafize Basak Bayraktar, and Alessandro Amato
Nat. Hazards Earth Syst. Sci., 21, 3713–3730, https://doi.org/10.5194/nhess-21-3713-2021, https://doi.org/10.5194/nhess-21-3713-2021, 2021
Short summary
Short summary
We investigated the seismic fault structure and the rupture characteristics of the MW 6.6, 2 May 2020, Cretan Passage earthquake through tsunami data inverse modelling. Our results suggest a shallow crustal event with a reverse mechanism within the accretionary wedge rather than on the Hellenic Arc subduction interface. The study identifies two possible ruptures: a steeply sloping reverse splay fault and a back-thrust rupture dipping south, with a more prominent dip angle.
Cited articles
Amato, A., Avallone, A., Basili, R., Bernardi, F., Brizuela, B., Graziani, L., Herrero, A., Lorenzino, M. C., Lorito, S., Mele, F. M., Michelini, A., Piatanesi, A., Pintore, S., Romano, F., Selva, J., Stramondo, S., Tonini, R., and Volpe, M.: From seismic monitoring to tsunami warning in the Mediterranean Sea, Seismol. Res. Lett., 92, 1796–1816, https://doi.org/10.1785/0220200437, 2021. a, b
Barbe, P., Cicone, A., Li, W. S., and Zhou, H.: Time-frequency representation of nonstationary signals: the IMFogram, arXiv [preprint], https://doi.org/10.48550/arXiv.2011.14209, 2020. a, b, c
Beltrami, G. M.: An ANN algorithm for automatic, real-time tsunami detection in deep-sea level measurements, Ocean Eng., 35, 572–587, https://doi.org/10.1016/j.oceaneng.2007.11.009, 2008. a, b, c
Beltrami, G. M.: Automatic, real-time detection and characterization of tsunamis in deep-sea level measurements, Ocean Eng., 38, 1677–1685, https://doi.org/10.1016/j.oceaneng.2011.07.016, 2011. a, b, c, d
Bressan, L., Zaniboni, F., and Tinti, S.: Calibration of a real-time tsunami detection algorithm for sites with no instrumental tsunami records: application to coastal tide-gauge stations in eastern Sicily, Italy, Nat. Hazards Earth Syst. Sci., 13, 3129–3144, https://doi.org/10.5194/nhess-13-3129-2013, 2013. a
Cicone, A.: Iterative filtering as a direct method for the decomposition of nonstationary signals, Numer. Algorithms, 85, 811–827, https://doi.org/10.1007/s11075-019-00838-z, 2020. a, b, c
Cicone, A. and Zhou, H.: Numerical analysis for iterative filtering with new efficient implementations based on FFT, Numer. Math., 147, 1–28, https://doi.org/10.1007/s00211-020-01165-5, 2021. a, b
Cicone, A., Liu, J., and Zhou, H.: Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis, Appl. Comput. Harmon. A., 41, 384–411, https://doi.org/10.1016/j.acha.2016.03.001, 2016. a
Cicone, A., Li, W. S., and Zhou, H.: New theoretical insights in the decomposition and time-frequency representation of nonstationary signals: the IMFogram algorithm, arXiv [preprint], https://doi.org/10.48550/arXiv.2205.15702, 2022. a
Cicone, A., Li, W. S., and Zhou, H.: New theoretical insights in the decomposition and time-frequency representation of nonstationary signals: the imfogram algorithm, Appl. Comput. Harmon. A., 71, 101634, https://doi.org/10.1016/j.acha.2024.101634, 2024a. a, b, c
Cicone, A., Serra-Capizzano, S., and Zhou, H.: One or two frequencies? the iterative filtering answers, Appl. Math. Comput., 462, 128322, https://doi.org/10.1016/j.amc.2023.128322, 2024b. a
Codiga, D. L. Unified Tidal Analysis and Prediction Using the UTide Matlab Functions, Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, Technical Report 2011-01, 59 pp., https://doi.org/10.13140/RG.2.1.3761.2008, 2011. a, b
Consoli, S., Recupero, D. R., and Zavarella, V.: A survey on tidal analysis and forecasting methods for Tsunami detection, arXiv [preprint], https://doi.org/10.48550/arXiv.1403.0135, 2014. a
Duputel, Z., Rivera, L., Kanamori, H., Hayes, G. P., Hirshorn, B., and Weinstein, S.: Real-time W phase inversion during the 2011 off the Pacific coast of Tohoku Earthquake, Earth Planets Space, 63, 535–539, https://doi.org/10.5047/eps.2011.05.032, 2011. a
GNS Science: NZ Deep-ocean Assessment and Reporting of Tsunami (DART) Data set, GNS Science [data set], https://doi.org/10.21420/8TCZ-TV02?x=y, 2020. a
Goring, D. G.: Extracting long waves from tide-gauge records, J. Waterw. Port C.-ASCE, 134, 306–312, https://doi.org/10.1061/(ASCE)0733-950X(2008)134:5(306), 2008. a
Heidarzadeh, M., Wang, Y., Satake, K., and Mulia, I. E.: Potential deployment of offshore bottom pressure gauges and adoption of data assimilation for tsunami warning system in the western Mediterranean Sea, Geoscience Letters, 6, 1–12, https://doi.org/10.1186/s40562-019-0149-8, 2019. a
Howe, B. M., Arbic, B. K., Aucan, J., Barnes, C. R., Bayliff, N., Becker, N., Butler, R., Doyle, L., Elipot, S., Johnson, G. C., Landerer, F., Lentz, S., Luther, D. S., Müller, M., Mariano, J., Panayotou, K., Rowe, C., Ota, H., Song, Y. T., Thomas, M., Thomas, P. N., Thompson, P., Tilmann, F., Weber, T., and Weinstein, S.: SMART cables for observing the global ocean: science and implementation, Frontiers in Marine Science, 6, 424, https://doi.org/10.3389/fmars.2019.00424, 2019. a
Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., and Liu, H. H.: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis, P. Roy. Soc. Lond. A Mat., 454, 903–995, https://doi.org/10.1098/rspa.1998.0193, 1998. a, b
Kato, T., Terada, Y., Kinoshita, M., Kakimoto, H., Isshiki, H., Matsuishi, M., Yokoyama, A., and Tanno, T.: Real-time observation of tsunami by RTK-GPS, Earth Planets Space, 52, 841–845, https://doi.org/10.1186/BF03352292, 2000. a
Kawaguchi, K., Kaneda, Y., and Araki, E.: The DONET: A real-time seafloor research infrastructure for the precise earthquake and tsunami monitoring, in: OCEANS 2008-MTS/IEEE Kobe Techno-Ocean, IEEE, 1–4, https://doi.org/10.1109/OCEANSKOBE.2008.4530918, 2008. a
Lee, J.-W., Park, S.-C., Lee, D. K., and Lee, J. H.: Tsunami arrival time detection system applicable to discontinuous time series data with outliers, Nat. Hazards Earth Syst. Sci., 16, 2603–2622, https://doi.org/10.5194/nhess-16-2603-2016, 2016. a
Lomax, A. and Michelini, A.: Tsunami early warning within five minutes, Pure Appl. Geophys., 170, 1385–1395, https://doi.org/10.1007/s00024-012-0512-6, 2013. a
Maeda, T., Obara, K., Shinohara, M., Kanazawa, T., and Uehira, K.: Successive estimation of a tsunami wavefield without earthquake source data: A data assimilation approach toward real-time tsunami forecasting, Geophys. Res. Lett., 42, 7923–7932, https://doi.org/10.1002/2015GL065588, 2015. a
Marinaro, G., D'Amico, S., Embriaco, D., Giuntini, A., Simeone, F., O'Neill, J., Nicholson, B., Watkiss, N., and Restelli, F.: A 21 km SMART Cable for earthquakes and tsunami detection operating in the Ionian Sea, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16261, https://doi.org/10.5194/egusphere-egu24-16261, 2024. a
Matthäus, W.: On the history of recording tide gauges, Proc. R. Soc. Edin. B-Bi., 73, 26–34, https://doi.org/10.1017/S0080455X00002083, 1972. a
Mochizuki, M., Uehira, K., Kanazawa, T., Kunugi, T., Shiomi, K., Aoi, S., Matsumoto, T., Takahashi, N., Chikasada, N., Nakamura, T., Sekiguchi, S., Shinohara, M., and Yamada, T.: S-net project: Performance of a large-scale seafloor observation network for preventing and reducing seismic and tsunami disasters, in: 2018 OCEANS-MTS/IEEE Kobe Techno-Oceans (OTO), Kobe, Japan, 28–31 May 2018, IEEE, 1–4, https://doi.org/10.1109/OCEANSKOBE.2018.8558823, 2018. a
Mungov, G., Eblé, M., and Bouchard, R.: DART® tsunameter retrospective and real-time data: A reflection on 10 years of processing in support of tsunami research and operations, Pure Appl. Geophys., 170, 1369–1384, https://doi.org/10.1007/s00024-012-0477-5, 2013. a, b, c
National Oceanic and Atmospheric Administration: Deep-Ocean Assessment and Reporting of Tsunamis (DART(R)), NOAA National Centers for Environmental Information [data set], https://doi.org/10.7289/V5F18WNS, 2005. a
Okal, E. A.: Quantifying the Ocean Coupling of Air Waves, and Why DART Data Reporting Can Be Deceptive, Pure Appl. Geophys., 181, 1095–1115, https://doi.org/10.1007/s00024-024-03448-6, 2024. a
Pawlowicz, R., Beardsley, B., and Lentz, S.: Classical tidal harmonic analysis including error estimates in MATLAB using T_TIDE, Comput. Geosci., 28, 929–937, https://doi.org/10.1016/S0098-3004(02)00013-4, 2002. a
Rabinovich, A. B.: Spectral analysis of tsunami waves: Separation of source and topography effects, J. Geophys. Res.-Oceans, 102, 12663–12676, https://doi.org/10.1029/97JC00479, 1997. a
Rabinovich, A. B. and Eblé, M. C.: Deep-ocean measurements of tsunami waves, Pure Appl. Geophys., 172, 3281–3312, https://doi.org/10.1007/s00024-015-1058-1, 2015. a, b, c, d
Schafer, R. W.: What is a savitzky-golay filter? [lecture notes], IEEE Signal Proc. Mag., 28, 111–117, https://doi.org/10.1109/MSP.2011.941097, 2011. a
Selva, J., Amato, A., Armigliato, A., Basili, R., Bernardi, F., Brizuela, B., Cerminara, M., de’Micheli Vitturi, M., Di Bucci, D., Di Manna, P., Esposti Ongaro, T., Lacanna, G., Lorito, S., Løvholt, F., Mangione, D., Panunzi, E., Piatanesi, A., Ricciardi, A., Ripepe, M., Romano, F., Santini, M., Scalzo, A., Tonini, R., Volpe, M., and Zaniboni, F.: Tsunami risk management for crustal earthquakes and non-seismic sources in Italy, La Rivista del Nuovo Cimento, 44, 69–144, https://doi.org/10.1007/s40766-021-00016-9, 2021a. a
Selva, J., Lorito, S., Volpe, M., Romano, F., Tonini, R., Perfetti, P., Bernardi, F., Taroni, M., Scala, A., Babeyko, A., Løvholt, F., Gibbons, S. J., Macías, J., Castro, M. J., González-Vida, J. M., Sánchez-Linares, C., Bayraktar, H. B., Basili, R., Maesano, F. E., Tiberti, M. M., Mele, F., Piatanesi, A., and Amato, A.: Probabilistic tsunami forecasting for early warning, Nat. Commun., 12, 5677, https://doi.org/10.1038/s41467-021-25815-w, 2021b. a
Street, J. O., Carroll, R. J., and Ruppert, D.: A note on computing robust regression estimates via iteratively reweighted least squares, Am. Stat., 42, 152–154, https://doi.org/10.2307/2684491, 1988. a
Tang, L., Titov, V. V., and Chamberlin, C. D.: Development, testing, and applications of site-specific tsunami inundation models for real-time forecasting, J. Geophys. Res.-Oceans, 114, C12025, https://doi.org/10.1029/2009JC005476, 2009. a
Titov, V. V., González, F. I., Mofjeld, H. O., and Newman, J. C.: Short-term inundation forecasting for tsunamis, in: Submarine Landslides and Tsunamis, edited by: Yalçiner, A. C., Pelinovsky, E. N., Okal, E., and Synolakis, C. E., Springer, Dordrecht, 277–284, https://doi.org/10.1007/978-94-010-0205-9_29, 2003. a
Titov, V. V., Gonzalez, F. I., Bernard, E., Eble, M. C., Mofjeld, H. O., Newman, J. C., and Venturato, A. J.: Real-time tsunami forecasting: Challenges and solutions, Nat. Hazards, 35, 35–41, https://doi.org/10.1007/s11069-004-2403-3, 2005. a, b
Tsushima, H., Hino, R., Fujimoto, H., and Tanioka, Y.: Application of cabled offshore ocean bottom tsunami gauge data for real-time tsunami forecasting, in: 2007 Symposium on Underwater Technology and Workshop on Scientific Use of Submarine Cables and Related Technologies, Tokyo, Japan, 17–20 April 2007, IEEE, 612–620, https://doi.org/10.1109/UT.2007.370824, 2007. a
Wang, Y., Satake, K., Maeda, T., and Gusman, A. R.: Green's function-based tsunami data assimilation: A fast data assimilation approach toward tsunami early warning, Geophys. Res. Lett., 44, 10–282, https://doi.org/10.1002/2017GL075307, 2017. a
Wang, Y., Maeda, T., Satake, K., Heidarzadeh, M., Su, H.-Y., Sheehan, A., and Gusman, A. R.: Tsunami data assimilation without a dense observation network, Geophys. Res. Lett., 46, 2045–2053, https://doi.org/10.1029/2018GL080930, 2019a. a, b
Wang, Y., Satake, K., Cienfuegos, R., Quiroz, M., and Navarrete, P.: Far-field tsunami data assimilation for the 2015 Illapel earthquake, Geophys. J. Int., 219, 514–521, https://doi.org/10.1093/gji/ggz309, 2019b. a, b
Wang, Y., Satake, K., Maeda, T., Shinohara, M., and Sakai, S.: A method of real-time tsunami detection using ensemble empirical mode decomposition, Seismol. Res. Lett., 91, 2851–2861, https://doi.org/10.1785/0220200115, 2020. a, b, c, d
Wang, Y., Tsushima, H., Satake, K., and Navarrete, P.: Review on recent progress in near-field tsunami forecasting using offshore tsunami measurements: Source inversion and data assimilation, Pure Appl. Geophys., 178, 5109–5128, https://doi.org/10.1007/s00024-021-02910-z, 2021. a
Williamson, A. L. and Newman, A. V.: Suitability of open-ocean instrumentation for use in near-field tsunami early warning along seismically active subduction zones, Pure Appl. Geophys., 176, 3247–3262, https://doi.org/10.1007/s00024-018-1898-6, 2019. a
Wu, Z. and Huang, N. E.: Ensemble empirical mode decomposition: a noise-assisted data analysis method, Advances in Adaptive Data Analysis, 1, 1–41, https://doi.org/10.1142/S1793536909000047, 2009. a
Yasuda, T. and Mase, H.: Real-time tsunami prediction by inversion method using offshore observed GPS buoy data: nankaido, J. Waterw. Port C.-ASCE, 139, 221–231, https://doi.org/10.1061/(ASCE)WW.1943-5460.0000159, 2013. a
Short summary
To issue precise and timely tsunami alerts, detecting the propagating tsunami is fundamental. The most used instruments are pressure sensors positioned at the ocean bottom, called ocean-bottom pressure gauges (OBPGs). In this work, we study four different techniques that allow us to recognize a tsunami as soon as it is recorded by an OBPG and a methodology to calibrate them. The techniques are compared in terms of their ability to detect and characterize the tsunami wave in real time.
To issue precise and timely tsunami alerts, detecting the propagating tsunami is fundamental....
Altmetrics
Final-revised paper
Preprint