Seismic waves from earthquakes and other sources are used to infer the structure and
properties of Earth's interior. The availability of large-scale seismic datasets and the …

Machine learning (ML) is a collection of methods used to develop understanding and
predictive capability by learning relationships embedded in data. ML methods are becoming …

Seismic event detection and phase picking are the base of many seismological workflows. In
recent years, several publications demonstrated that deep learning approaches significantly …

The deep magmatic architecture of the Hawaiian volcanic system is central to understanding
the transport of magma from the upper mantle to the individual volcanoes. We leverage …

The need for an earthquake early-warning system (EEWS) is unavoidable to save lives. In
terms of managing earthquake disasters and achieving effective risk mitigation, the quick …

We propose to implement a compact convolutional transformer (CCT) for picking the
earthquake phase arrivals (EQCCT). The proposed method consists of two branches, with …

Abstract Distributed Acoustic Sensing (DAS) is an emerging technology for earthquake
monitoring and subsurface imaging. However, its distinct characteristics, such as unknown …

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 …

Neural phase pickers—neural networks designed and trained to pick seismic phase arrivals—
have proven to be a powerful tool for developing earthquake catalogs. However, these …

In recent years, artificial intelligence technology has exhibited great potential in seismic
signal recognition, setting off a new wave of research. Vast amounts of high-quality labeled …