Recently deep learning (DL), as a new data‐driven technique compared to conventional
approaches, has attracted increasing attention in geophysical community, resulting in many …

Earthquake early warning (EEW) is a relatively new strategy for reducing disaster risk and
increasing resilience to seismic hazard in urban settings. EEW systems provide real-time …

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

An immediate report of the source focal mechanism with full automation after a destructive
earthquake is crucial for timely characterizing the faulting geometry, evaluating the stress …

In this letter, we use deep neural networks for unsupervised clustering of seismic data. We
perform the clustering in a feature space that is simultaneously optimized with the clustering …

The Italian earthquake waveform data are collected here in a dataset suited for machine
learning analysis (ML) applications. The dataset consists of nearly 1.2 million three …

Earthquake monitoring in urban settings is essential but challenging, due to the strong
anthropogenic noise inherent to urban seismic recordings. Here, we develop a deep …

During July 2019, a sequence of earthquakes, including an M w 6.4 foreshock and an M w
7.1 mainshock, occurred near Ridgecrest, California. ShakeAlert, the US Geological Survey …

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 …

Abstract Machine‐learning (ML) methods have seen widespread adoption in seismology in
recent years. The ability of these techniques to efficiently infer the statistical properties of …