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 …

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 …

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 …

Detecting earthquake arrivals within seismic time series can be a challenging task. Visual,
human detection has long been considered the gold standard but requires intensive manual …

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

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

Robust estimation of ground motions generated by scenario earthquakes is critical for many
engineering applications. We leverage recent advances in generative adversarial networks …

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

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

Identifying appropriate seismic events is the primary precondition for conducting meaningful
analysis in seismological research. The successful creation of a method to automatically …