scholarly journals Supplementary material to "Ground motion prediction maps using seismic microzonation data and machine learning"

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Ground Motion ◽  
Seismic Microzonation ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Supplementary Material

scholarly journals Ground motion prediction maps using seismic microzonation data and machine learning

2021 ◽  
Author(s):  
Federico Mori ◽  
Amerigo Mendicelli ◽  
Gaetano Falcone ◽  
Gianluca Acunzo ◽  
Rose Line Spacagna ◽  
...  
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Ground Motion ◽  
Scale Effects ◽  
Earthquake Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction ◽  
Intensity Measures ◽  
Local Site ◽  
Ground Motion Variability

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.

Machine learning in ground motion prediction

2021 ◽  
Vol 148 ◽  
pp. 104700
Author(s):  
Farid Khosravikia ◽  
Patricia Clayton
Keyword(s):  
Machine Learning ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction

scholarly journals Ground Motion Prediction Equations for Malaysia Due to Subduction Zone Earthquakes in Sumatran Region

IEEE Access ◽  
2017 ◽  
Vol 5 ◽  
pp. 23920-23937
Author(s):  
M. S. Liew ◽  
Kamaluddeen Usman Danyaro ◽  
Mazlina Mohamad ◽  
Lim Eu Shawn ◽  
Aziz Aulov
Keyword(s):  
Subduction Zone ◽  
Ground Motion ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Subduction Zone Earthquakes

Ground-motion model for subduction earthquakes in northern South America

2021 ◽  
pp. 875529302110275
Author(s):  
Carlos A Arteta ◽  
Cesar A Pajaro ◽  
Vicente Mercado ◽  
Julián Montejo ◽  
Mónica Arcila ◽  
...  
Keyword(s):  
South America ◽  
Ground Motion ◽  
Ground Motions ◽  
Strong Motion ◽  
Motion Prediction ◽  
Depth Range ◽  
Ground Motion Prediction ◽  
Natural Period ◽  
Nazca Plate ◽  
Northern South America

Subduction ground motions in northern South America are about a factor of 2 smaller than the ground motions for similar events in other regions. Nevertheless, historical and recent large-interface and intermediate-depth slab earthquakes of moment magnitudes Mw = 7.8 (Ecuador, 2016) and 7.2 (Colombia, 2012) evidenced the vast potential damage that vulnerable populations close to earthquake epicenters could experience. This article proposes a new empirical ground-motion prediction model for subduction events in northern South America, a regionalization of the global AG2020 ground-motion prediction equations. An updated ground-motion database curated by the Colombian Geological Survey is employed. It comprises recordings from earthquakes associated with the subduction of the Nazca plate gathered by the National Strong Motion Network in Colombia and by the Institute of Geophysics at Escuela Politécnica Nacional in Ecuador. The regional terms of our model are estimated with 539 records from 60 subduction events in Colombia and Ecuador with epicenters in the range of −0.6° to 7.6°N and 75.5° to 79.6°W, with Mw≥4.5, hypocentral depth range of 4 ≤  Zhypo ≤ 210 km, for distances up to 350 km. The model includes forearc and backarc terms to account for larger attenuation at backarc sites for slab events and site categorization based on natural period. The proposed model corrects the median AG2020 global model to better account for the larger attenuation of local ground motions and includes a partially non-ergodic variance model.

scholarly journals Ground motions in urban Los Angeles from the 2019 Ridgecrest earthquake sequence

2021 ◽  
pp. 875529302110039
Author(s):  
Filippos Filippitzis ◽  
Monica D Kohler ◽  
Thomas H Heaton ◽  
Robert W Graves ◽  
Robert W Clayton ◽  
...  
Keyword(s):  
Los Angeles ◽  
Ground Motion ◽  
Ground Motions ◽  
Earthquake Sequence ◽  
Motion Prediction ◽  
Ground Motion Prediction Equations ◽  
Prediction Equations ◽  
Ground Motion Prediction ◽  
Velocity Models ◽  
Spectral Accelerations

We study ground-motion response in urban Los Angeles during the two largest events (M7.1 and M6.4) of the 2019 Ridgecrest earthquake sequence using recordings from multiple regional seismic networks as well as a subset of 350 stations from the much denser Community Seismic Network. In the first part of our study, we examine the observed response spectral (pseudo) accelerations for a selection of periods of engineering significance (1, 3, 6, and 8 s). Significant ground-motion amplification is present and reproducible between the two events. For the longer periods, coherent spectral acceleration patterns are visible throughout the Los Angeles Basin, while for the shorter periods, the motions are less spatially coherent. However, coherence is still observable at smaller length scales due to the high spatial density of the measurements. Examining possible correlations of the computed response spectral accelerations with basement depth and Vs30, we find the correlations to be stronger for the longer periods. In the second part of the study, we test the performance of two state-of-the-art methods for estimating ground motions for the largest event of the Ridgecrest earthquake sequence, namely three-dimensional (3D) finite-difference simulations and ground motion prediction equations. For the simulations, we are interested in the performance of the two Southern California Earthquake Center 3D community velocity models (CVM-S and CVM-H). For the ground motion prediction equations, we consider four of the 2014 Next Generation Attenuation-West2 Project equations. For some cases, the methods match the observations reasonably well; however, neither approach is able to reproduce the specific locations of the maximum response spectral accelerations or match the details of the observed amplification patterns.

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