Articles | Volume 22, issue 3
https://doi.org/10.5194/nhess-22-947-2022
© Author(s) 2022. 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-22-947-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
22 Mar 2022
Research article |
| 22 Mar 2022
| 22 Mar 2022
Ground motion prediction maps using seismic-microzonation data and machine learning
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Amerigo Mendicelli
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Gaetano Falcone
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Gianluca Acunzo
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Rose Line Spacagna
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
Giuseppe Naso
Presidenza del Consiglio dei Ministri, Dipartimento della Protezione
Civile (DPC), via Vitorchiano 2, 00189 Rome, Italy
Massimiliano Moscatelli
Consiglio Nazionale delle Ricerche (CNR-IGAG), Istituto di Geologia
Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km
29.300, 00015 Monterotondo (Rome), Italy
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Cited
22 citations as recorded by crossref.
- Empirical shaking scenarios for Europe: a feasibility study D. Bindi et al. https://doi.org/10.1093/gji/ggac382
- Explainable Machine-Learning Predictions for Peak Ground Acceleration R. Sun et al. https://doi.org/10.3390/app13074530
- Machine learning and its integration with nonlinear ground response analysis in Bhaktapur District, Nepal D. Poudyal Lohani et al. https://doi.org/10.1080/17486025.2025.2596759
- Ground motion modelling with bidirectional liquid neural network (BLiqNet) P. Neelamraju & R. STG https://doi.org/10.1016/j.geoai.2026.100080
- Real-time reconstruction of ground motion during small magnitude earthquakes: A pilot study Y. Kim et al. https://doi.org/10.1016/j.soildyn.2025.109707
- A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California M. Monterrubio-Velasco et al. https://doi.org/10.1038/s43247-024-01436-1
- A scenario-based approach for immediate post-earthquake rockfall impact assessment M. Alvioli et al. https://doi.org/10.1007/s10346-023-02127-2
- Analysis of earthquake-prone areas based on the seismic wave velocity, young's modulus, shear modulus, and Poisson’s ratio for disaster risk reduction in Bengkulu city, Indonesia A. Hadi et al. https://doi.org/10.1007/s11069-024-06827-3
- City-scale ground motion mapping from building-embedded structural response sensors using building-specific hybrid deep learning models A. Zar et al. https://doi.org/10.1007/s10518-026-02659-7
- A ground motion prediction model for the Italian region based on a mixture of experts framework J. Dai et al. https://doi.org/10.5194/nhess-26-3541-2026
- Enhanced Seismic Ground Motion Modeling With Conditional Variational Autoencoder P. Neelamraju et al. https://doi.org/10.1002/eer2.70006
- Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts P. Lemenkova https://doi.org/10.3390/data7060074
- Machine learning-based ground motion model with finite-fault distance metrics F. Mori et al. https://doi.org/10.1007/s11069-026-08304-5
- Seismic microzonation and soil-structure resonance analysis in Suryabinayak Municipality, Bhaktapur, Nepal: insights from ambient vibration measurements D. Sakhakarmi et al. https://doi.org/10.1080/19475705.2024.2311892
- Seismic site categorization and H800 map for Lisbon: the first and second generations of Eurocode 8 L. Oliveira et al. https://doi.org/10.1007/s10518-026-02552-3
- Seismic resilience-based strategies for prioritization of interventions on a subregional area M. Vona et al. https://doi.org/10.1007/s10518-024-02072-y
- Conditional generative adversarial networks for the generation of strong ground motion parameters using KiK-net ground motion records Z. Ba et al. https://doi.org/10.1016/j.asoc.2025.112730
- A Framework for Seismic Risk Assessments in the Buyeo Baekje Historic Areas K. Ji et al. https://doi.org/10.1080/15583058.2026.2646960
- Seismic Acceleration Estimation Method at Arbitrary Position Using Observations and Machine Learning K. Lee et al. https://doi.org/10.1007/s12205-022-1235-6
- The Prediction Model of Seismic Variation in Complex Terrain based on the BP Neural Network with Cavities Y. Li & H. Zhou https://doi.org/10.1007/s00024-024-03589-8
- Comprehensive seismic hazard mapping for North Bengkulu, Southwest Sumatera, Indonesia: Planning for urban resilience and Safer cities A. Hadi et al. https://doi.org/10.1016/j.soildyn.2026.110182
- Training and validation of a machine learning model for seismic vulnerability of masonry buildings in aggregate and selection of key parameters S. Pinasco et al. https://doi.org/10.1016/j.istruc.2025.109773
22 citations as recorded by crossref.
- Empirical shaking scenarios for Europe: a feasibility study D. Bindi et al. https://doi.org/10.1093/gji/ggac382
- Explainable Machine-Learning Predictions for Peak Ground Acceleration R. Sun et al. https://doi.org/10.3390/app13074530
- Machine learning and its integration with nonlinear ground response analysis in Bhaktapur District, Nepal D. Poudyal Lohani et al. https://doi.org/10.1080/17486025.2025.2596759
- Ground motion modelling with bidirectional liquid neural network (BLiqNet) P. Neelamraju & R. STG https://doi.org/10.1016/j.geoai.2026.100080
- Real-time reconstruction of ground motion during small magnitude earthquakes: A pilot study Y. Kim et al. https://doi.org/10.1016/j.soildyn.2025.109707
- A machine learning estimator trained on synthetic data for real-time earthquake ground-shaking predictions in Southern California M. Monterrubio-Velasco et al. https://doi.org/10.1038/s43247-024-01436-1
- A scenario-based approach for immediate post-earthquake rockfall impact assessment M. Alvioli et al. https://doi.org/10.1007/s10346-023-02127-2
- Analysis of earthquake-prone areas based on the seismic wave velocity, young's modulus, shear modulus, and Poisson’s ratio for disaster risk reduction in Bengkulu city, Indonesia A. Hadi et al. https://doi.org/10.1007/s11069-024-06827-3
- City-scale ground motion mapping from building-embedded structural response sensors using building-specific hybrid deep learning models A. Zar et al. https://doi.org/10.1007/s10518-026-02659-7
- A ground motion prediction model for the Italian region based on a mixture of experts framework J. Dai et al. https://doi.org/10.5194/nhess-26-3541-2026
- Enhanced Seismic Ground Motion Modeling With Conditional Variational Autoencoder P. Neelamraju et al. https://doi.org/10.1002/eer2.70006
- Handling Dataset with Geophysical and Geological Variables on the Bolivian Andes by the GMT Scripts P. Lemenkova https://doi.org/10.3390/data7060074
- Machine learning-based ground motion model with finite-fault distance metrics F. Mori et al. https://doi.org/10.1007/s11069-026-08304-5
- Seismic microzonation and soil-structure resonance analysis in Suryabinayak Municipality, Bhaktapur, Nepal: insights from ambient vibration measurements D. Sakhakarmi et al. https://doi.org/10.1080/19475705.2024.2311892
- Seismic site categorization and H800 map for Lisbon: the first and second generations of Eurocode 8 L. Oliveira et al. https://doi.org/10.1007/s10518-026-02552-3
- Seismic resilience-based strategies for prioritization of interventions on a subregional area M. Vona et al. https://doi.org/10.1007/s10518-024-02072-y
- Conditional generative adversarial networks for the generation of strong ground motion parameters using KiK-net ground motion records Z. Ba et al. https://doi.org/10.1016/j.asoc.2025.112730
- A Framework for Seismic Risk Assessments in the Buyeo Baekje Historic Areas K. Ji et al. https://doi.org/10.1080/15583058.2026.2646960
- Seismic Acceleration Estimation Method at Arbitrary Position Using Observations and Machine Learning K. Lee et al. https://doi.org/10.1007/s12205-022-1235-6
- The Prediction Model of Seismic Variation in Complex Terrain based on the BP Neural Network with Cavities Y. Li & H. Zhou https://doi.org/10.1007/s00024-024-03589-8
- Comprehensive seismic hazard mapping for North Bengkulu, Southwest Sumatera, Indonesia: Planning for urban resilience and Safer cities A. Hadi et al. https://doi.org/10.1016/j.soildyn.2026.110182
- Training and validation of a machine learning model for seismic vulnerability of masonry buildings in aggregate and selection of key parameters S. Pinasco et al. https://doi.org/10.1016/j.istruc.2025.109773
Saved (final revised paper)
Latest update: 17 Sep 2026
Short summary
This work addresses the problem of the ground motion estimation over large areas as an important tool for seismic-risk reduction policies. In detail, the near-real-time estimation of ground motion is a key issue for emergency system management. Starting from this consideration, the present work proposes the application of a machine learning approach to produce ground motion maps, using nine input proxies. Such proxies consider seismological, geophysical, and morphological parameters.
This work addresses the problem of the ground motion estimation over large areas as an important...
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