Preprints
https://doi.org/10.5194/nhess-2021-282
https://doi.org/10.5194/nhess-2021-282

  04 Oct 2021

04 Oct 2021

Review status: this preprint is currently under review for the journal NHESS.

Ground motion prediction maps using seismic microzonation data and machine learning

Federico Mori1, Amerigo Mendicelli1, Gaetano Falcone1, Gianluca Acunzo1, Rose Line Spacagna1, Giuseppe Naso2, and Massimiliano Moscatelli1 Federico Mori et al.
  • 1CNR-IGAG, Consiglio Nazionale delle Ricerche, Istituto di Geologia Ambientale e Geoingegneria, Area della Ricerca di Roma 1, Via Salaria km 29.300, 00015 Monterotondo (Roma), Italy
  • 2Presidenza del Consiglio dei Ministri, Dipartimento della Protezione Civile (DPC), via Vitorchiano 2, 00189 Roma, Italy

Abstract. Past seismic events worldwide demonstrated that damage and death toll depend on both the strong ground motion (i.e., source effects) and the local site effects. The variability of earthquake ground motion distribution is caused by local stratigraphic and/or topographic setting and buried morphologies, that can give rise to amplification and resonances with respect to the ground motion expected at the reference site. Therefore, local site conditions can affect an area with damage related to the full collapse or loss in functionality of facilities, roads, pipelines, and other lifelines. To this concern, the near real time prediction of damage pattern over large areas is a crucial issue to support the rescue and operational interventions. A machine learning approach was adopted to produce ground motion prediction maps considering both stratigraphic and morphological conditions. A set of about 16'000 accelometric data and about 46'000 geological and geophysical data were retrieved from Italian and European databases. The intensity measures of interest were estimated based on 9 input proxies. The adopted machine learning regression model (i.e., Gaussian Process Regression) allows to improve both the precision and the accuracy in the estimation of the intensity measures with respect to the available near real time predictions methods (i.e., Ground Motion Prediction Equation and shaking maps). In addition, maps with a 50 × 50 m resolution were generated providing a ground motion variability in agreement with the results of advanced numerical simulations based on detailed sub-soil models. The variability at short distances (hundreds of meters) was demonstrated to be responsible for 30–40 % of the total variability of the predicted IM maps, making it desirable that seismic hazard maps also consider short-scale effects.

Federico Mori et al.

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on nhess-2021-282', Anonymous Referee #1, 16 Oct 2021
  • RC2: 'Comment on nhess-2021-282', Anonymous Referee #2, 03 Nov 2021

Federico Mori et al.

Federico Mori et al.

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Short summary
This work addresses the problem of the ground motion estimation over large area as an important tool for the seismic risk reduction policies. In detail, the near real time estimation of ground motion is a key issue for the emergency system management. Starting from this consideration, the present work proposes the application of a machine learning approach to produce ground motion maps, using 9 input proxies. Such proxies consider seismological, geophysical and morphological parameters.
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