Earthquake waveforms are necessary for simulation, verification, and validation during the
development of structural and earthquake engineering. However, due to the fact that the …

Detecting earthquake events from seismic time series has proved itself a challenging task.
Manual detection can be expensive and tedious due to the intensive labor and large scale …

Deep learning (DL) methodologies have been recently employed to solve various civil and
earthquake engineering problems. Nevertheless, due to the limited number of reliable data …

While generative adversarial network (GAN) models have shown success in generating
synthetic data of acoustic, image, and speech, research on generating seismic waves using …

Earthquake event detection in seismic time series data is an important and challenging
problem. The current state-of-the-art machine-learning based detection methods mostly …

Within seismology, geology, civil and structural engineering, deep learning (DL), especially
via generative adversarial networks (GANs), represents an innovative, engaging, and …

We develop a ground-motion model (GMM) for crustal earthquakes in Japan that can directly
model the probability distribution of ground-motion acceleration time histories based on …

Detecting earthquake events from seismic time series signal is a challenging task. Recently,
detection methods based on machine learning have been developed to improve the …

Realistic synthetic data can be useful for data augmentation when training deep learning
models to improve seismological detection and classification performance. In recent years …

Effective response strategies to earthquake disasters are crucial for disaster management in
smart cities. However, in regions where earthquakes do not occur frequently, model …