Ground motion prediction maps using seismic-microzonation data and machine learning

Mori, Federico; Mendicelli, Amerigo; Falcone, Gaetano; Acunzo, Gianluca; Spacagna, Rose Line; Naso, Giuseppe; Moscatelli, Massimiliano

doi:https://doi.org/10.5194/nhess-22-947-2022

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.

Special issue:

Earthquake-induced hazards: ground motion amplification and...

https://doi.org/10.5194/nhess-22-947-2022

© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Articles | Volume 22, issue 3

Research article

|

22 Mar 2022

Research article |

| 22 Mar 2022

Ground motion prediction maps using seismic-microzonation data and machine learning

Federico Mori, Amerigo Mendicelli, Gaetano Falcone, Gianluca Acunzo, Rose Line Spacagna, Giuseppe Naso, and Massimiliano Moscatelli

Supplement

https://doi.org/10.5194/nhess-22-947-2022-supplement

Data sets

Engineering Strong Motion Database (ESM) flatfile Istituto Nazionale di Geofisica e Vulcanologia (INGV) https://data.ingv.it/dataset/404#additional-metadata

NEar-Source Strong-motion flatfile (NESS) Istituto Nazionale di Geofisica e Vulcanologia (INGV) https://data.ingv.it/dataset/446#additional-metadata

Data for: A new Vs30 map for Italy based on the seismic microzonation dataset Federico Mori, Amerigo Mendicelli, Massimiliano Moscatelli, Gino Romagnoli, Edoardo Peronace, and Giuseppe Naso https://doi.org/10.17632/8458tgzc73.1

ALOS Global Digital Surface Model "ALOS World 3D - 30m (AW3D30)" Advanced Land Observing Satellite (ALOS) https://www.eorc.jaxa.jp/ALOS/en/dataset/aw3d30/aw3d30_e.htm

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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.