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Probabilistic seismic hazard and risk models are essential to improving our awareness of seismic risk, to its management, and to increasing our resilience against earthquake disasters. These models consist of a series of components, which…

Geophysics · Physics 2024-01-30 K Trevlopoulos , P Gehl , C Negulescu

Forecasts of the focal mechanisms of future earthquakes are important for seismic hazard estimates and Coulomb stress and other models of earthquake occurrence. Here we report on a high-resolution global forecast of earthquake rate density…

Geophysics · Physics 2015-06-17 Yan Y. Kagan , David D. Jackson

Estimates of seismic wave speeds in the Earth (seismic velocity models) are key input parameters to earthquake simulations for ground motion prediction. Owing to the non-uniqueness of the seismic inverse problem, typically many velocity…

Two different way of assessing seismic vulnerability are available nowadays: observed or empirical and calculated vulnerability assessment methods. The first methods are based on observed damage after earthquakes correlated with the…

Geophysics · Physics 2007-10-09 Clotaire Michel , Philippe Guéguen

Ground-motion prediction equations (GMPE) are essential in probabilistic seismic hazard studies for estimating the ground motions generated by the seismic sources. In low seismicity regions, only weak motions are available in the lifetime…

During the past two decades, the use of ambient vibrations for modal analysis of structures has increased as compared to the traditional techniques (forced vibrations). The Frequency Domain Decomposition method is nowadays widely used in…

Geophysics · Physics 2008-05-13 Clotaire Michel , Philippe Guéguen , Pierre-Yves Bard

P-wave first-motion polarity plays an important role in resolving focal mechanisms of small to moderate earthquakes (M <= 4.5). High-quality focal mechanism solutions for abundant small events can greatly improve our understanding of…

Geophysics · Physics 2025-11-25 Ziye Yu , Yuqi Cai

In countries with a moderate seismic hazard, the classical methods developed for strong motion prone countries to estimate the seismic behaviour and subsequent vulnerability of existing buildings are often inadequate and not financially…

Assessing the vulnerability of a large set of buildings using sophisticated methods can be very time consuming and at a prohibitive cost, particularly for a moderate seismic hazard country like France. We propose here a low-cost analysis…

Geophysics · Physics 2007-09-26 Clotaire Michel , Philippe Guéguen

Focal mechanisms of small earthquakes are critical for characterizing faults and regional stress. P-wave polarities and S/P amplitude ratios (e.g., in HASH) are widely used to determine focal mechanisms for small earthquakes, but…

Geophysics · Physics 2026-03-31 Miao Zhang

The ground structure can substantially influence seismic ground motion underscoring the need to develop a ground structure model with sufficient reliability in terms of ground motion estimation for earthquake damage mitigation. While many…

Computational Engineering, Finance, and Science · Computer Science 2024-07-02 Tsuyoshi Ichimura , Kohei Fujita , Ryota Kusakabe , Hiroyuki Fujiwara , Muneo Hori , Maddegedara Lalith

Bayesian neural networks (BNN) are the probabilistic model that combines the strengths of both neural network (NN) and stochastic processes. As a result, BNN can combat overfitting and perform well in applications where data is limited.…

Machine Learning · Statistics 2023-04-13 Sabber Ahamed , Md Mesbah Uddin

For decades, classical point process models, such as the epidemic-type aftershock sequence (ETAS) model, have been widely used for forecasting the event times and locations of earthquakes. Recent advances have led to Neural Point Processes…

Geophysics · Physics 2026-03-12 Samuel Stockman , Daniel Lawson , Maximilian Werner

This paper presents a comparative analysis of structural seismic responses under two types of ground motion inputs: (i) synthetic motions generated by stochastic spectral-compatible ground motion models and (ii) recorded motions from an…

Geophysics · Physics 2026-01-01 Jungho Kim , Maijia Su , Ziqi Wang , Marco Broccardo

Seismic vulnerability analysis of existing buildings requires basic information on their structural behaviour. The ambient vibrations of buildings and the modal parameters (frequencies, damping ration and modal shapes) that can be extracted…

Geophysics · Physics 2009-07-13 Clotaire Michel , Philippe Guéguen

Near real-time damage diagnosis of building structures after extreme events (e.g., earthquakes) is of great importance in structural health monitoring. Unlike conventional methods that are usually time-consuming and require human expertise,…

Signal Processing · Electrical Eng. & Systems 2019-10-25 Seyed Omid Sajedi , Xiao Liang

Earthquake occurrence is notoriously difficult to predict. While some aspects of their spatiotemporal statistics can be relatively well captured by point-process models, very little is known regarding the magnitude of future events, and it…

Geophysics · Physics 2026-04-29 Neri Berman , Oleg Zlydenko , Oren Gilon , Yossi Matias , Yohai Bar-Sinai

Accurate prediction of dynamical response of structural system depends on the correct modeling of that system. However, modeling becomes increasingly challenging when there are many candidate models available to describe the system…

Applications · Statistics 2025-10-06 Subhayan De , Tianhao Yu , Patrick T. Brewick , Erik A. Johnson , Steven F. Wojtkiewicz

Quantification of the impact of uncertainty in material properties as well as the input ground motion on structural responses is an important step in implementing a performance-based earthquake engineering (PBEE) framework. Among various…

Computational Engineering, Finance, and Science · Computer Science 2020-08-12 Mohammad Amin Hariri-Ardebili , Farhad Pourkamali-Anaraki , Siamak Sattar

Accurate prediction of structural failure modes under seismic excitations is essential for seismic risk and resilience assessment. Traditional simulation-based approaches often result in imbalanced datasets dominated by non-failure or…

Machine Learning · Computer Science 2026-02-12 Jungho Kim , Taeyong Kim
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