[HTML][HTML] Innovations in earthquake risk reduction for resilience: Recent advances and challenges

F Freddi, C Galasso, G Cremen, A Dall'Asta… - International Journal of …, 2021 - Elsevier
Abstract The Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR) highlights
the importance of scientific research, supporting the 'availability and application of science …

The 2023 US 50-state national seismic hazard model: Overview and implications

MD Petersen, AM Shumway, PM Powers… - Earthquake …, 2024 - journals.sagepub.com
The US National Seismic Hazard Model (NSHM) was updated in 2023 for all 50 states using
new science on seismicity, fault ruptures, ground motions, and probabilistic techniques to …

NGA-West2 equations for predicting PGA, PGV, and 5% damped PSA for shallow crustal earthquakes

DM Boore, JP Stewart, E Seyhan… - Earthquake …, 2014 - journals.sagepub.com
We provide ground motion prediction equations for computing medians and standard
deviations of average horizontal component intensity measures (IMs) for shallow crustal …

NGA-West2 ground motion model for the average horizontal components of PGA, PGV, and 5% damped linear acceleration response spectra

KW Campbell, Y Bozorgnia - Earthquake Spectra, 2014 - journals.sagepub.com
We used an expanded PEER NGA-West2 database to develop a new ground motion
prediction equation (GMPE) for the average horizontal components of PGA, PGV, and 5 …

Update of the Chiou and Youngs NGA model for the average horizontal component of peak ground motion and response spectra

BSJ Chiou, RR Youngs - Earthquake Spectra, 2014 - journals.sagepub.com
We present an update to our 2008 NGA model for predicting horizontal ground motion
amplitudes caused by shallow crustal earthquakes occurring in active tectonic …

Machine learning in seismology: Turning data into insights

Q Kong, DT Trugman, ZE Ross… - Seismological …, 2019 - pubs.geoscienceworld.org
This article provides an overview of current applications of machine learning (ML) in
seismology. ML techniques are becoming increasingly widespread in seismology, with …

STanford EArthquake Dataset (STEAD): A global data set of seismic signals for AI

SM Mousavi, Y Sheng, W Zhu, GC Beroza - IEEE Access, 2019 - ieeexplore.ieee.org
Seismology is a data rich and data-driven science. Application of machine learning for
gaining new insights from seismic data is a rapidly evolving sub-field of seismology. The …

PEER NGA-east database

CA Goulet, T Kishida, TD Ancheta… - Earthquake …, 2021 - journals.sagepub.com
This article documents the earthquake ground motion database developed for the NGA-East
Project, initiated as part of the Next Generation Attenuation (NGA) research program and led …

Probabilistic seismic hazard analysis at regional and national scales: State of the art and future challenges

MC Gerstenberger, W Marzocchi, T Allen… - Reviews of …, 2020 - Wiley Online Library
Seismic hazard modeling is a multidisciplinary science that aims to forecast earthquake
occurrence and its resultant ground shaking. Such models consist of a probabilistic …

Machine learning in ground motion prediction

F Khosravikia, P Clayton - Computers & Geosciences, 2021 - Elsevier
This paper studies the advantages and disadvantages of different machine learning
techniques in predicting ground-motion intensity measures given source characteristics …